Network Intrusion Detection Systems (NIDS) are now increasingly leveraging Machine Learning (ML) techniques to detect malicious network activities. Numerous papers have scrutinized the security of ML-based NIDS (ML-NIDS) by testing them against various attacks involving adversarial perturbations. The findings were oftentimes worrying: by making imperceptible changes to a given input, powerful ML models would be bypassed. In this context, we took a step back and wondered: where (i.e., in what “space”) have these perturbations been applied?

Machine learning (ML)–based intrusion detection systems (IDS) often degrade when deployed across heterogeneous networks due to domain shifts in traffic and configuration. To mitigate this degradation, conventional domain adaptation (DA) methods aim to align source and target data distributions; however, they require access to source data during deployment—an impractical constraint that undermines scalability and reusability. To overcome this limitation, we propose TRANSFA-IDS (Transformer Source-Free Adaptation for IDS), which removes the need for source data during adaptation while preserving the knowledge encoded in the source-trained model. TRANSFA-IDS transforms tabular flow records into structured color image embeddings and employs a compact Vision Transformer with a Deep Support Vector Data Description (Deep-SVDD) head to learn domain-invariant representations of benign behavior. During deployment, it adapts to new environments using only a small portion of unlabeled target traffic by fine-tuning the last Transformer block, efficiently realigning feature distributions without retraining. Experiments across cross-dataset settings (CICIDS2018↔UNSW-NB15) show that TRANSFA-IDS achieves AUROC scores up to 0.908 and 0.873, outperforming traditional non-adaptive unsupervised baselines by over 40% while adapting more than twice as fast as conventional adaptive unsupervised. These results demonstrate that source-free adaptation can deliver both high accuracy and deployment practicality for scalable IDS across diverse network environments.

Current Virtual Reality (VR) systems rely on inside-out visual-inertial tracking, which enables accurate localization but provides only a partial representation of the user's body. This limitation restricts embodiment and interaction fidelity in interactive VR scenarios requiring full-body awareness and expressive gestures. To capture both global body motion and fine-grained interaction cues within a single sensing framework, we introduce MultiSenseVR, the first open multimodal dataset that jointly captures synchronized millimeter-wave (mmWave) Wi-Fi, Surface Electromyography (sEMG), inertial signals, and high-precision 3D motion capture for ground truth in an immersive VR setting. The dataset includes recordings from 24 participants interacting with a custom fast-food simulation designed to elicit natural full-body movement. In addition to objective sensing data, MultiSenseVR provides subjective measures of presence and cybersickness. Baseline evaluations show that mmWave Wi-Fi sensing supports 3D pose estimation with accuracy comparable to camera-based approaches, while sEMG enables accurate subject-specific grasp classification. The dataset and supporting code are publicly available at https://osf.io/f6r7d.

As immersive media becomes more accessible, virtual counterparts to real-world experiences such as concerts and conferences have emerged, enabled by volumetric streaming pipelines for virtual reality (VR). User representation is central to these systems, with point clouds widely adopted for realistic avatars due to their balance between quality and performance. However, most systems struggle to scale to larger user counts. While recent work has proposed more scalable architectures, these typically focus on computational optimizations and fail to scale to high user counts due to network bottlenecks. To address this gap, we present an open-source, modular, distributed WebRTC-based volumetric streaming pipeline that employs multiple selective forwarding units (SFUs) to improve latency, throughput, and quality compared to a centralized SFU. Results show that, with 64 users, the distributed setup receives 147% more points with comparable transport latency, and reduces peak latency at lower user counts. Furthermore, leveraging quality adaptation enables stable latency across scales, achieving approximately 25 ms.

Immersive technologies promise to revolutionize communication through enhanced sense of presence and interactivity. To enable interaction, reliable low-latency transport mechanisms are needed to handle the large volumes of data created by complex 3D objects. In this paper, we propose an open-source, codec-independent, selective forwarding unit (SFU) for real-time volumetric video streaming using WebRTC. For evaluation purposes, we provide a reference client implementation by extending VR2Gather, a TCP-based system for immersive communication. We conduct extensive evaluations using both new and existing datasets to compare the performance of WebRTC against TCP-based protocols in an emulated testbed environment. The evaluations demonstrate that WebRTC outperforms other protocols in high-latency scenarios and adapts video quality to user movement 13% and 36% faster than its TCP-based counterparts in networks with 5 ms and 10 ms of network latency, respectively.

As Extended Reality (XR) transitions from individual experiences to multi-user collaborative environments, understanding the dynamics of team connection and cooperation becomes critical. This pilot study investigates the potential of Physiological Synchrony (PS) as an objective measure of team chemistry in Collaborative Virtual Reality (CVR). We conduct a within-subjects study where participant pairs engage in a pizza-making task both in a collaborative and in a competitive scenario. Physiological data, i.e. Galvanic Skin Response (GSR), Photoplethysmogram (PPG), and Interbeat Interval (IBI), are collected and analyzed to quantify synchrony levels and compared to subjective questionnaires. Results confirm that participants perceive significantly higher team chemistry in the collaborative scenario. Objectively, PPG shows a preliminary tendency towards synchrony compared to subjective scores.

This paper investigates the impact of interactioninduced body movement on cybersickness in Virtual Reality (VR). Therefore we designed a controlled VR environment with three interaction conditions of increasing movement complexity. Data from 24 participants, combining subjective measures and motionderived features, show a consistent increase in cybersickness with higher movement demands, particularly in conditions involving vertical displacement and full-body interaction. Moreover, correlation analysis reveals a significant negative relationship between the coefficient of variation of head velocity and cybersickness. This suggests that more variable and adaptive motion may mitigate the discomfort. These findings highlight the importance of movement characteristics, beyond movement intensity alone, for designing full-body interactive VR experiences.

Knowledge graph embeddings can be trained to infer which missing facts are likely to be true. For this, false training examples need to be derived from the available set of positive facts, so that the embedding models can learn to recognize the boundary between fact and fiction. Various negative sampling strategies have been proposed to tackle this issue, some of which have tried to make use of axiomatic knowledge claims to minimize the number of nonsensical negative samples being generated. By putting constraints on the construction of each candidate sample, these techniques have tried to maximize the number of true negatives outputted by the procedure. However, such strategies rely exclusively on binary interpretations of constraint-based reasoning and have so far also failed to incorporate literal-valued entities into the negative sampling procedure. To alleviate these shortcomings, we propose a negative sampling strategy based on a combination of fuzzy set theory and strict axiomatic semantics, which allow for the incorporation of literal-awareness when determining domain or range membership values. When evaluated on benchmark datasets AIFB and MUTAG, we found that these improvements offered significant performance gains across multiple metrics with respect to state of the art negative sampling techniques, suggesting that fuzzy semantics and literal-awareness can help to improve the quality of generated negative samples. On AIFB, our fuzzy negative sampling approach outperforms baselines on four metrics, with performance gains up to 17.14%. On MUTAG, our fuzzy negative sampling approach outperforms baselines on eight metrics, with performance gains up to 55.49%.

The rapid growth of Artificial Intelligence (AI) workloads has introduced unprecedented challenges to modern cloud-native systems, particularly in Kubernetes (K8s)-based environments. These workloads often demand low-latency communication, high resource locality, and efficient utilization of heterogeneous hardware devices such as Graphics Processing Units (GPUs) and specialized accelerators. However, the existing scheduling mechanisms in K8s are typically unaware of the underlying physical topology, leading to performance degradation and inefficient resource usage. This paper presents Sakkara, a novel topology-aware scheduling framework designed to optimize the placement of AI workloads in K8s clusters. Sakkara incorporates a hierarchical model of the Data Center (DC), including nodes and racks, enabling flexible scheduling strategies that account for resource availability and risk-aware metrics that mitigate performance interference and constraint violations caused by topology-unaware placement. Sakkara extends existing scheduling logic in K8s with placement strategies that guide pod allocation using configurable topology constraints, aiming to minimize communication costs and maximize workload performance. We evaluated Sakkara on a representative AI workload, a distributed training application under different cluster configurations. Experimental results show that Sakkara improves job completion time, throughput, and memory utilization compared to available K8s schedulers, achieving improvements of up to 10%. Sakkara, available as open-source, offers a promising pathway toward topology-conscious orchestration of AI workloads in next-generation cloud environments.

Video consumption has become central to modern life, with users seeking more immersive experiences such as virtual conferencing or concerts within virtual reality (VR). While 360° video offers rotational movement, it lacks true positional freedom. Fully immersive formats like light fields and volumetric video enable six degrees-of-freedom (6DoF), allowing both types of freedom. However, their high bandwidth and computational demands make them impractical for low-latency applications. Efforts to address these issues through compression and quality adaptation have improved quality of experience (QoE), but real-time interaction remains limited because of latency. To solve this, we introduce a novel, open-source one-to-many streaming architecture using point cloud-based volumetric video. By compressing point clouds with the Draco codec and transmitting via web real-time communication (WebRTC), we achieve low-latency 6DoF streaming. Content is adapted by employing a multiple description coding (MDC) strategy which combines sampled point cloud descriptions using the estimated bandwidth returned by the Google congestion control (GCC) algorithm. MDC encoding scales more easily to a larger number of users compared to individual encoding. Our proposed solution achieves similar real-time latency for both three and eight clients ( \(163\,\mathrm{m}\mathrm{s}\) and \(166\,\mathrm{m}\mathrm{s}\) ), which is 9% and 19% lower compared to individual encoding. The MDC-based approach, using three workers, achieves similar visual quality compared to a per client encoding solution using five worker threads, and increased quality when the number of clients is greater than 20. Additionally, when compared to an approach with five fixed quality levels, our MDC-based approach scores 13% better in terms of latency, while achieving similar quality.

The demand for high-quality, low-latency video streaming is placing strain on conventional internet infrastructures. This article proposes a hybrid unicast–broadcast video delivery framework designed to address this challenge by integrating advanced 5G broadcast technologies with traditional unicast methods. By offloading popular content to a broadcast network, the approach aims to alleviate congestion and enhance overall streaming efficiency. To ensure reliable video segment delivery over the broadcast network, regardless of the physical layer, we incorporate Packet Recovery (PR) and Forward Error Correction (FEC) mechanisms. Additionally, Temporal Layer Injection (TLI) is employed to further improve video quality while maintaining reduced bandwidth requirements compared to traditional unicast-only approaches. This innovative framework leverages 5G terrestrial broadcasting within Over-the-Top (OTT) streaming environments, enabling seamless delivery of adaptive video content with sub-1-second live latency. Comprehensive experimentation and evaluation through large-scale emulation demonstrate the efficacy of this hybrid approach in meeting the evolving demands of modern multimedia delivery systems. Notably, when broadcasting the top three most commonly watched video streams, 63% of viewers no longer need to request video segments via unicast, as they are efficiently delivered over broadcast channels. This hybrid model offers significant scalability, cost reduction for an ISP, and efficiently delivers content directly to user devices without additional intermediaries, improving viewer experience through low-latency, high-quality streaming.

Continuum computing has emerged as a paradigm to improve various aspects of service orchestration by offloading computation from the cloud to the network edge. However, edge orchestration poses two significant challenges compared to cloud computing. On one hand, cloud software scheduling algorithms make suboptimal decisions when applied to the network edge, as edge devices and networks are more hetereogeneous than cloud data centers, and orchestration requires different parameters. On the other hand, most orchestration platforms assume highly centralized cloud data centers, with each server running many easily migrated software instances, whereas edge devices have limited hardware capabilities and migration of tasks between devices is significantly slower than in the cloud. As a result, there is a need for a decentralized orchestration platform that allows scheduling algorithms to take into account a wide variety of device properties and deployment requirements in placement decisions. This article presents Flocky, a decentralized device discovery and service orchestration framework based on Open Application Model (OAM), to address this gap. The architecture of Flocky is elaborated, showing how OAM enables flexible intent modeling in the edge, and combined with a Gossip-like algorithm allows individual edge devices to discover devices in their neighborhoods, map their capabilities, and optimally deploy parts of applications to individual nodes. Evaluation shows Flocky to be highly scalable and mainly dependent on local node density, with nodes discovering over 97% of their viable neighbours on average within two discovery rounds, while using 84% less memory than a centralized orchestrator such as Kubernetes.

eXtended Reality (XR) and holographic telepresence place stringent Quality of Service (QoS) demands on network infrastructure, requiring ultra-low latency, high throughput, and reliable connectivity. Meeting such QoS demands is critical in dynamic, distributed cloud environments, but does not always guarantee a satisfactory user experience. Quality of Experience (QoE) captures the user’s perception of service performance, which may be influenced by factors not fully reflected in systemlevel metrics. Thus, novel orchestration strategies must consider both QoS and QoE. This paper proposes a Reinforcement Learning (RL)-driven approach to edge-cloud orchestration capable of adapting to dynamic network conditions, leveraging a multiobjective reward function, including both QoS and QoE aspects, to guide service placement decisions. Evaluation shows that our RL approach reaches a 21.3% QoE gain over heuristics and 14.7% over balanced strategies, with 100% request acceptance. The results highlight the robustness and scalability of RL-driven orchestration, particularly for latency-sensitive 6G applications. Our findings also reveal the limitations of traditional heuristics under complex objectives and highlight the potential of RL as a transformative tool for intelligent network and service management in next-generation communication systems.

The emergence of the 6 G era demands seamless orchestration across an increasingly heterogeneous and distributed compute continuum-spanning edge, fog, and cloud resources. Moreover, the next generation of the mobile network is poised to redefine the digital landscape by enabling pervasive intelligence, ultra-low latency communication, and extreme heterogeneity across the entire network infrastructure. This transformation introduces unprecedented orchestration challenges due to the dynamic, multi-domain, and resource-constrained nature of emerging workloads such as Generative Artificial Intelligence (GenAI) inference, immersive eXtended Reality (XR), and autonomous systems. To tackle this complexity, we advocate for a Hybrid Hierarchical Digital Twin (DT) architecture that serves as a foundation for intelligent, adaptive, and real-time orchestration in 6 G environments. We present a comprehensive vision for integrating DTs as enablers of intelligent, context-aware, and adaptive orchestration mechanisms that span across multiple domains. The proposed architecture introduces a multi-layered DT hierarchy combining local and global views, enabling scalable coordination and real-time decision-making. We highlight key architectural enhancements required to realize this vision, including inter-twin interoperability and behavioral modeling for QoE estimation. This work aims to guide researchers and practitioners in shaping the foundations of resilient and efficient orchestration frameworks for 6 G systems.

Current networking solutions are unable to satisfy the low-latency requirements of real-time volumetric video conferencing when faced with heavy congestion scenarios. Traditional congestion controllers use packet loss or the change in round-trip time (RTT) to estimate the bandwidth. Commonly, this method is too slow as congestion has already occurred and the receiving user has already experienced a latency spike. Low latency, low loss and scalable throughput (L4S), recently published as RFC 9330, wants to alleviate this problem by aiming for sub 1 ms queuing delay for low-latency traffic by using accurate explicit congestion notification (AccECN) packet marking to notify applications of early congestion. We propose an L4S-based pipeline for volumetric video delivery, which achieves a more consistent latency under congestion compared to web real-time communication (WebRTC). In addition, L4S bandwidth estimation achieves a 45% faster convergence compared to Google congestion control (GCC) estimation, commonly used in WebRTC. Furthermore, in our detailed evaluation setup the L4S application experiences no packet loss, while the WebRTC-based version suffers from irrecoverable packet loss, resulting in 3% of frames being undecodable.

The exponential growth in Artificial Intelligence (AI) adoption presents unique challenges and opportunities for deploying AI workloads in modern Data Center (DC) networks, particularly in terms of performance, scalability, and reliability. AI workloads, such as inference and distributed training, impose different network demands: inference is primarily computebound and typically requires low network latency, while distributed training is network-bound and requires high bandwidth, placing significant strain on the network. This paper focuses on the network requirements of widely known AI communication patterns, and studies their impact on modern DC architectures by analyzing the effects of different orchestration strategies-specifically packing and spreading-on throughput, response time, and network congestion. The results show that packing strategies generally deliver higher performance for most covered AI collectives. However, spreading strategies can be beneficial in certain scenarios, such as when larger workloads span across higher number of racks, as they can help mitigate network congestion between the switches of leaf-spine network configurations. This paper offers valuable insights into optimizing the orchestration of popular AI collectives in data center networks, presenting informed strategies to improve performance in response to growing AI demands, with findings demonstrating completion time reductions of up to 30 %.

The rapid adoption of containerized cloud environments requires robust and efficient Auto-Scaling (AS) mechanisms to ensure adequate resource utilization, high performance, and cost-effectiveness. Traditional AS approaches, often based on predefined thresholds, fail to adapt well to dynamic workloads. This paper investigates the potential of Reinforcement Learning (RL) as a generalized solution for efficient AS in containerized clouds. Building on previous studies, this paper examines whether RL approaches can learn adaptive scaling policies when trained on diverse workload datasets and tested across different scenarios. A Multi-Objective (MO) reward function has been designed to optimize key performance factors such as the application's response time, and resource utilization. The results demonstrate that RL algorithms can effectively balance competing objectives and adapt to changing workloads. The Latency strategy resulted in lower latency but required more pods (7.4) and slightly higher CPU usage (28.92%). In contrast, the Cost strategy minimized deployment costs with fewer pods (3.56) and lower CPU usage (24.45%). This study highlights the versatility and efficiency of RL in managing complex, real-time scaling decisions in containerized cloud infrastructures.

The rapid expansion of Internet of Things (IoT), edge, and embedded devices in the past decade has introduced numerous challenges in terms of security and configuration management. Simultaneously, advances in cloud-native development practices have greatly enhanced the development experience and facilitated quicker updates, thereby enhancing application security. However, applying these advances to IoT, edge, and embedded devices remains a complex task, primarily due to the heterogeneous environments and the need to support devices with extended lifespans. WebAssembly and the WebAssembly System Interface (WASI) has emerged as a promising technology to bridge this gap. As WebAssembly becomes more popular on IoT, edge, and embedded devices, there is a growing demand for hardware interface support in WebAssembly programs. This work presents WASI proposals and proof-of-concept implementations to enable hardware interaction with I2C and USB, which are two commonly used protocols in IoT, directly from WebAssembly applications. This is achieved by running the device drivers within WebAssembly as well. A thorough evaluation of the proof of concepts shows that WASI-USB introduces a minimal overhead of at most 8% compared to native operating system USB APIs. However, the results show that runtime initialization overhead can be significant in low-latency applications.

With the advent of containerization technologies, microservices have revolutionized application deployment by converting old monolithic software into a group of loosely coupled containers, aiming to offer greater flexibility and improve operational efficiency. This transition made applications more complex, consisting of tens to hundreds of microservices. Designing effective orchestration mechanisms remains a crucial challenge, especially for emerging distributed cloud paradigms such as the Compute Continuum (CC). Orchestration across multiple clusters is still not extensively explored in the literature since most works consider single-cluster scenarios. In the CC scenario, the orchestrator must decide the optimal locations for each microservice, deciding whether instances are deployed altogether or placed across different clusters, significantly increasing orchestration complexity. This paper addresses orchestration in a containerized CC environment by studying a Reinforcement Learning (RL) approach for efficient microservice deployment in Kubernetes (K8s) clusters, a widely adopted container orchestration platform. This work demonstrates the effectiveness of RL in achieving near-optimal deployment schemes under dynamic conditions, where network latency and resource capacity fluctuate. We extensively evaluate a multi-objective reward function that aims to minimize overall latency, reduce deployment costs, and promote fair distribution of microservice instances, and we compare it against typical heuristic-based approaches. The results from an implemented OpenAI Gym framework, named as HephaestusForge, show that RL algorithms achieve minimal rejection rates (as low as 0.002%, 90x less than the baseline Karmada scheduler). Cost-aware strategies result in lower deployment costs (2.5 units), and latency-aware functions achieve lower latency (268–290 ms), improving by 1.5x and 1.3x, respectively, over the best-performing baselines. HephaestusForge is available in a public open-source repository, allowing researchers to validate their own placement algorithms. This study also highlights the adaptability of the DeepSets (DS) neural network in optimizing microservice placement across diverse multi-cluster setups without retraining. The DS neural network can handle inputs and outputs as arbitrarily sized sets, enabling the RL algorithm to learn a policy not bound to a fixed number of clusters.

Containers have reshaped application deployment and life-cycle management in recent cloud platforms. The paradigm shift from large monolithic applications to complex graphs of loosely-coupled microservices aims to increase deployment flexibility and operational efficiency. However, efficient allocation and scaling of microservice applications is challenging due to their intricate inter-dependencies. Existing works do not consider microservice dependencies, which could lead to the application’s performance degradation when service demand increases. As dependencies increase, communication between microservices becomes more complex and frequent, leading to slower response times and higher resource consumption, especially during high demand. In addition, performance issues in one microservice can also trigger a ripple effect across dependent services, exacerbating the performance degradation across the entire application. This paper studies the impact of microservice inter-dependencies in auto-scaling by proposing Gwydion , a novel framework that enables different auto-scaling goals through Reinforcement Learning (RL) algorithms. Gwydion has been developed based on the OpenAI Gym library and customized for the popular Kubernetes (K8s) platform to bridge the gap between RL and auto-scaling research by training RL algorithms on real cloud environments for two opposing reward strategies: cost-aware and latency-aware. Gwydion focuses on improving resource usage and reducing the application’s response time by considering microservice inter-dependencies when scaling horizontally. Experiments with microservice benchmark applications , such as Redis Cluster (RC) and Online Boutique (OB), show that RL agents can reduce deployment costs and the application’s response time compared to default scaling mechanisms , achieving up to 50% lower latency while avoiding performance degradation. For RC, cost-aware algorithms can reduce the number of deployed pods (2 to 4), resulting in slightly higher latency ( 300 μ s to 6 ms) but lower resource consumption. For OB, all RL algorithms exhibit a notable response time improvement by considering all microservices in the observation space, enabling the sequential triggering of actions across different deployments. This leads to nearly 30% cost savings while maintaining consistently lower latency throughout the experiment. Gwydion aims to advance auto-scaling research in a rapidly evolving dynamic cloud environment.

The emergence of cloud computing has driven advancements in software virtualization, particularly microservice containerization. This in turn led to the development of Container Network Interfaces (CNIs) such as Flannel to connect microservices over a network. Despite their objective to provide connectivity, CNIs have not been adequately benchmarked when containers are connected over an external network. This creates uncertainty about the operation reliability of CNIs in distributed edge-cloud ecosystems. Given the multitude of available CNIs and the complexity of comparing different ones, this paper focuses on the widely adopted CNI, Flannel. It proposes the design of novel benchmarks of Flannel across external networks, Software Defined Networking (SDN)-based and non-SDN, characterizing two of the key backend types of Flannel: User Datagram Protocol (UDP) and Virtual Extensible LAN (VXLAN). Unlike existing benchmarks, this study analysis the overhead introduced by the external network and the impact of network disruptions. The paper outlines the systematic approach to benchmarking a set of Key Performance Indicators (KPIs), including: speed, latency and throughput. A variety of network disruptions have been induced to analyse their impact on these KPIs, including: delay, packet loss, and packet corruption. The results show that VXLAN consistently outperforms UDP, offering superior bandwidth with efficient resource consumption, making it more suitable for production environments. In contrast, the UDP backend is suitable for real-time video streaming applications due to its higher data rate and lower jitter, though it requires higher resource utilization. Moreover, the results show less variation in KPIs over SDN, compared to non-SDN. The benchmark data are made publicly available in an open-source repository, enabling researchers to replicate the experiments, and potentially extend the study to other CNIs. This work contributes to the network management domain by providing an extensive benchmark study on container networking highlighting the main advantages and disadvantages of current technologies.

Recent years have seen the adoption of workload orchestration into the network edge. Cloud orchestrators such as Kubernetes have been extended to edge computing, providing the virtual infrastructure to efficiently manage containerized workloads across the edge-cloud continuum. However, cloud-based orchestrators are resource intensive, sometimes occupying the bulk of resources of an edge device even when idle. While various Kubernetes-based solutions, such as K3s and KubeEdge, have been developed with a specific focus on edge computing, they remain limited to container runtimes. This paper proposes a Kubernetes-compatible solution for edge workload packaging, distribution, and execution, named Feather, which extends edge workloads beyond containers. Feather is based on Virtual Kubelets, superseding previous work from FLEDGE. It is capable of operating in existing Kubernetes clusters, with minimal, optional additions to the Kubernetes PodSpec to enable multi-runtime images and execution. Both Containerd and OSv unikernel backends are implemented, and evaluations show that unikernel workloads can be executed highly efficiently, with a memory reduction of up to 20% for Java applications at the cost of up to 25% CPU power. Evaluations also show that Feather itself is suitable for most modern edge devices, with the x86 version only requiring 58-62 MiB of memory for the agent itself.

Orchestrating microservice applications deployed on a federation of globally distributed Kubernetes clusters is a challenging and multifaceted optimization problem. It is not only computationally hard, but also requires balancing a delicate trade-off between competing performance metrics, such as latency, deployment cost, and service interruption frequency. Classical approaches in the literature merge multiple objectives into a single one via, e.g., linear combinations. However, in practice, it is complex to express a priori a quantitative preference between heterogeneous objectives, let alone with simple linear combinations. This paper adopts a more comprehensive approach leveraging proper Multi-Objective Optimization (MOO), with the goal of producing multiple solutions from the Pareto Front (PF). Therefore, the orchestrator can inspect a posteriori all possible "optimal" trade-offs and decide on the strategy that best fits their operating requirements. To solve the MOO problem, this paper adopts state-of-the-art Multi-Objective Evolutionary Algorithms and shows their effectiveness in solving the MOO problem. Illustrative results highlight the practical benefits of a MOO formulation, providing several tens of nondominated solutions and evenly covering the objectives’ space.

In response to the growing demand for low-latency video streaming, this paper presents a demonstration of a hybrid unicast-broadcast video delivery system that combines 5G terrestrial broadcasting with over-the-top (OTT) streaming methods. The demonstration features a scalable setup with an interactive dashboard, allowing users to experiment with various configurations and observe key metrics such as bandwidth usage, packet loss, buffer size, and live latency in real-time. Key techniques include Low-Latency DASH (LL-DASH) for HTTP Adaptive Streaming (HAS), packet recovery (PR) and Forward Error Correction (FEC) for reliability, Temporal Layer Injection (TLI) for enhanced quality, and Common Media Application Format (CMAF) with Chunked Transfer Encoding (CTE) for reduced latency. The demonstration shows that this scalable hybrid approach can effectively reduce unicast bandwidth to nearly 0 Mb/s in scenarios without packet loss on the broadcast network, and achieve similar bandwidth reductions in lossy broadcast networks with appropriate Forward Error Correction (FEC) settings, while maintaining a live latency lower than 1 second. These results demonstrate the system's potential for optimizing multimedia delivery, significantly reducing unicast bandwidth while maintaining low-latency streaming.

Traditional Intrusion Detection Systems (IDSs) have been a cornerstone of network security for many years. Nevertheless, with the advent of containerized applications in the last few years, there is a growing need to understand how intrusion detection can adapt to these dynamic environments. This paper presents ChronosGuard, a hierarchical machine learning (ML) IDS designed for containerized environments. ChronosGuard’s adaptable architecture consists of multiple components, each optimized for deployment in varying configurations ranging from monolithic to micro-service architectures. The performance impact of various factors such as network topology, work-load orchestration, and deployment strategies has been assessed through extensive experiments concerning the scalability and resource utilization of ChronosGuard. Results show the effective prioritization of benign traffic of up to 85% compared to malicious traffic, the negligible impact of small network delays on performance metrics, and up to 10% decrease in response times with network-aware orchestration for complex deployment configurations. This study introduces a robust, containerized IDS that can be easily adapted to meet various operational needs, ranging from a full privacy-preserving local deployment to a scalable cloud deployment but also provides foundational insights for future research into optimizing containerized security solutions.

With the advent of container technology, companies have been developing microservice-based applications, converting the old monolithic software into a group of loosely coupled containers, with the aim of offering greater flexibility and improving operational efficiency. When users access microservices, their initial point of contact is typically a load balancer. This component is responsible for distributing incoming traffic or requests between multiple instances of microservices. Traditional load balancing approaches mainly rely on round-robin, or weighted roundrobin algorithms which are inadequate to maintain the overall performance and scalability of microservice-based applications. Microservices are often deployed in dynamic environments needing a more adaptive and efficient load balancing strategy to optimize resources and reduce the overall latency for end users. This paper presents a dynamic load balancer for Kubernetes (K8s) clusters based on Reinforcement Learning (RL). It aims to minimize the overall latency while promoting fair distribution of requests. To achieve this goal, the load balancer considers both current network delays and processing loads in the cluster. The evaluation shows that our solution is effective even in environments where both the network traffic and the processing loads in the cluster change dynamically over time. In addition, this study highlights the flexibility of DeepSets neural networks in solving the load balancing challenge in diverse setups without retraining. The results show that the DeepSets algorithms can solve the microservice load balancing problem even in scenarios up to 30 times larger than the trained setup.

A Kubernetes cluster typically consists of trusted nodes, running within the confines of a physically secure datacenter. With recent advances in edge orchestration, this is no longer the case. This poses a new challenge: how can we trust a device that an attacker has physical access to? This paper presents an architecture and open-source implementation that securely enrolls edge devices as trusted Kubernetes worker nodes. By providing boot attestation rooted in a hardware Trusted Platform Module, a strong base of trust is provided. A new custom controller directs a modified version of Keylime to cross the cloud-edge gap and securely deliver unique cluster credentials required to enroll an edge worker. The controller dynamically grants and revokes these credentials based on attestation events, preventing a possibly compromised node from accessing sensitive cluster resources. We provide both a qualitative and a quantitative evaluation of the architecture. The qualitative scenarios prove its ability to attest and enroll an edge device with role-based access control (RBAC) permissions that dynamically adjust to attestation events. The quantitative evaluation reflects an average of 10.28 seconds delay incurred on the startup time of the edge node due to attestation for a total average enrollment time of 20.91 seconds. The presented architecture thus provides a strong base of trust, securing a physically exposed edge device and paving the way for a robust and resilient edge computing ecosystem.

The production and consumption of video content has become a staple in the current day and age. With the rise of virtual reality (VR), users are now looking for immersive, interactive experiences which combine the classic video applications, such as conferencing or digital concerts, with newer technologies. By going beyond 2D video into a 360 degree experience the first step was made. However, a 360 degree video offers only rotational movement, making interaction with the environment difficult. Fully immersive 3D content formats, such as light fields and volumetric video, aspire to go further by enabling six degrees-of-freedom (6DoF), allowing both rotational and positional freedom. Nevertheless, the adoption of immersive video capturing and rendering methods has been hindered by their substantial bandwidth and computational requirements, rendering them in most cases impractical for low latency applications. Several efforts have been made to alleviate these problems by introducing specialized compression algorithms and by utilizing existing 2D adaptation methods to adapt the quality based on the user's available bandwidth. However, even though these methods improve the quality of experience (QoE) and bandwidth limitations, they still suffer from high latency which makes real-time interaction unfeasible. To address this issue, we present a novel, open source [32], one-to-many streaming architecture using point cloud-based volumetric video. To reduce the bandwidth requirements, we utilize the Draco codec to compress the point clouds before they are transmitted using WebRTC which ensures low latency, enabling the streaming of real-time 6DoF interactive volumetric video. Content is adapted by employing a multiple description coding (MDC) strategy which combines sampled point cloud descriptions based on the estimated bandwidth returned by the Google congestion control (GCC) algorithm. MDC encoding scales more easily to a larger number of users compared to performing individual encoding. Our proposed solution achieves similar real-time latency for both three and nine clients (163 ms and 166 ms), which is 9% and 19% lower compared to individual encoding. The MDC-based approach, using three workers, achieves similar visual quality compared to a per client encoding solution, using five worker threads, and increased quality when the number of clients is greater than 20.

In today's world, the use of video conferencing applications has risen significantly. However, with the introduction of affordable head-mounted displays (HMDs), users are now seeking new immersive and engaging experiences that enhance the 2D video conferencing applications with a third dimension. Immersive video formats such as light fields and volumetric video aim to enhance the experience by allowing for six degrees-of-freedom (6DoF), resulting in users being able to look and walk around in the virtual space. We present a novel, open source, many-to-many streaming architecture using point cloud-based volumetric video. To ensure bitrates that satisfy contemporary networks, the Draco codec encodes the point clouds before they are transmitted using web real-time communication (WebRTC), all while ensuring that the end-to-end latency remains acceptable for real-time communication. A multiple description coding (MDC)-based quality adaptation approach ensures that the pipeline can support a large number of users, each with varying network conditions.

The future of human interaction is virtual. Thus it will require effective collaboration on tasks among users in remote settings. eXtended Reality (XR) is playing a leading role in this transition, offering a realm where virtual collaboration becomes not just possible but essential in situations where physical presence is limited by risk, cost, or complexity. However, while networks are continuously evolving, they can still introduce unexpected impairments that potentially degrade the user perception, i.e., the Quality-of-Experience (QoE), of such Collaborative Virtual Reality (CVR) scenarios. In response to this challenge, this paper presents a demonstrator designed to explicitly showcase the effects of network conditions on CVR. Our platform, centered around a pizza-making game, allows for exploration of the real-time impact of different network parameters, such as packet delay, loss, and throttling on the user engagement and perception in CVR. The framework employs a combination of subjective, objective, and physiological assessments, including the capture of heart rate and skin conductivity, to gain comprehensive insights into user experiences. Our platform not only allows users to directly experience the impact of network impairments on CVR interactions but also provides initial evidence of how such distortions affect both subjective perceptions and objective performance metrics.

Microservices have revolutionized application deployment in popular cloud platforms, offering flexible scheduling of loosely-coupled containers and improving operational efficiency. However, this transition made applications more complex, consisting of tens to hundreds of microservices. Efficient orchestration remains an enormous challenge, especially with emerging paradigms such as Fog Computing and novel use cases as autonomous vehicles. Also, multi-cluster scenarios are still not vastly explored today since most literature focuses mainly on a single-cluster setup. The scheduling problem becomes significantly more challenging since the orchestrator needs to find optimal locations for each microservice while deciding whether instances are deployed altogether or placed into different clusters. This paper studies the multi-cluster orchestration challenge by proposing a Reinforcement Learning (RL)-based approach for efficient microservice deployment in Kubernetes (K8s), a widely adopted container orchestration platform. The study demonstrates the effectiveness of RL agents in achieving near-optimal allocation schemes, emphasizing latency reduction and deployment cost minimization. Additionally, the work highlights the versatility of the DeepSets neural network in optimizing microservice placement across diverse multi-cluster setups without retraining. Results show that DeepSets algorithms optimize the placement of microservices in a multi-cluster setup 32 times higher than its trained scenario.

The increasing demand for high-quality video streaming, coupled with the necessity for low-latency delivery, presents significant challenges in today's multimedia landscape. In response to these challenges, this research explores the optimization of adaptive video streaming by integrating 5G terrestrial broadcasting with over-the-top (OTT) streaming methods. A comprehensive integration of forward error correction (FEC), temporal layer injection (TLI), and broadcast techniques enhance the robustness and efficiency of content delivery over broadcast networks and reduce unicast bandwidth to zero in low loss environments. Multiple strategies are compared through an extensive emulation setup for reducing latency in the end-to-end video delivery chain to sub 3-second live latency, demonstrating the effectiveness of a hybrid unicast-broadcast approach in achieving low-latency while maintaining high-quality video streaming performance with significantly reduced bandwidth. For 62.99% of viewers, unicast bandwidth can be reduced to as low as zero when broadcasting the top 3 TV channels.

EXtended Reality (XR) is already demonstrating its potential beyond the entertainment and gaming industry. One sector clearly benefiting from the advantages of XR is the treatment of stress related mental illnesses by means of Virtual Reality (VR). This is a form of therapy using VR which seeks to help decrease the intensity of the stress responses and anxiety levels due to the various modern-day pressures (e.g., situations, thoughts, or memories which provoke anxiety or fear). While showing promising results, the audiovisual essence of Virtual Reality (VR) can limit the effectiveness of this type of virtual therapy, as the patient’s interaction with the environment is constrained to their visual or at most also their audio senses. As such, including in the immersive therapy tactile therapy could enhance the experience and thus the effectiveness of the therapy. However, this has been largely unexplored. The purpose of this paper is to explore the impact of haptic feedback in reducing anxiety for stress relief treatment. Therefore, we present a haptic-enabled subjective methodology. As use case, we selected the booming case of the Virtual Rage Room (VRR), where participants can vent their rage by (virtually) destroying objects. The results of our study highlight the significantly positive impact of incorporating haptic feedback in mitigating anxiety within this context. Moreover, the analysis reassures the intrinsic value of this treatment as a potent tool for anxiety alleviation.

Knowledge Graphs (KGs) are gaining popularity and are being widely used in a plethora of applications. They owe their popularity to the fact that KGs are an ideal form to integrate and retrieve data originating from various sources. Using KGs as input for Machine Learning (ML) tasks allows to perform predictions on these popular graph structures. However, KGs cannot directly be used as ML input in their graph representation, they first require to be transformed to a vector space representation through an embedding technique. As ML techniques are data-driven, they can generalize over unseen input data that deviates to some extent from the data they were trained upon. To fully exploit the generalization capabilities of ML algorithms when using embedded KGs as input, small changes in the KGs should also result in small changes in the embedding space. Various embedding techniques for graphs in general exist, however, they have not been tailored towards embedding whole KGs, while KGs can be considered a special kind of graph that adheres to a certain KG schema. This paper evaluates if these existing embedding techniques that embed the whole graphs can represent the similarity between KGs in their embedding space, allowing ML algorithms to generalize over their input. We compare the similarities between KGs in terms of changes in size, entity labels, and KG schema. We found that most techniques were able to represent the similarities in terms of size and entity labels in their embedding space, however, none of the techniques were able to capture the similarities in KG schema.

In recent years, workload containerisation has been extended to the edge, bringing with it the need for flexible overlay networking. However, current container networking solutions are generally designed for the cloud, aimed at relatively static clusters with centralized generation of container subnet addresses and assigning them to nodes. Added to that existing tunneling solutions, such as Virtual Private Networks (VPN), also have centralized components. Conversely, the network edge is geo-dispersed and has a volatile topology,with edge nodes typically hidden behind routers, in private networks. To enable large-scale networking at the edge, there is need for decentralized self-management of container network addresses and overlay tunnels. This manuscript presents Warrens, a framework for fully decentralized and self-organizing cloud-edge container networks. Warrens enables communication between edge nodes in different private networks by enabling connectionless tunnels, supported by decentralized self-assignment of container IP addresses, with the assignment scheme minimizing address conflict to a negligible level. Warrens has been implemented in two variants using kernel-level eBPF for processing speed, and user-level Golang for wider compatibility. Warrens is shown to be highly scalable compared to a typical VPN solution, and performance evaluations demonstrate it can handle a full network load on both x64 devices and a Raspberry Pi with$\approx 0.5\%$to 5% total CPU load, depending on traffic direction and protocols used.

Containerization has revolutionized application deployments in current cloud platforms, enabling the flexible instantiation of loosely-coupled microservices and enhancing operational efficacy. However, optimizing the performance of container-based applications remains a challenge and a major topic in cloud research. This paper studies the impact of queue sorting in application scheduling, focused on complex inter-dependencies among microservices. Queue sorting determines the deployment order of containers in the infrastructure, typically based on container priorities and resource requests. Optimizing these algorithms directly influences scheduling efficiency and overall application performance. This paper compares several schedulers and sorting algorithms, leveraging extensive benchmark tests conducted on the widely-used Kubernetes (K8s) platform. The evaluation includes a novel sorting algorithm named Topological-Sort, designed to prioritize containers for application scheduling focused on microservice inter-dependencies. Results show the significant impact of queue sorting on application performance, with TopologicalSort algorithms outperforming default mechanisms, yielding an average increase of 20 % in throughput and reducing response time by at least 15 %. These results highlight the importance of considering microservice inter-dependencies for effective application deployment in modern container-based environments.

Machine learning (ML) has been a staple of academic research into pattern recognition in many fields, including cybersecurity.The momentum of ML continues to speed up alongside the advances in hardware capabilities and the methods they unlock, primarily (deep) neural networks.However, this article aims to demonstrate that the non-judicious use of ML in two prominent domains of data-based cybersecurity consistently misleads researchers into believing that their proposed methods constitute actual improvements.Armed with 17 stateof-the-art datasets in traffic and malware classification and the simplest possible machine learning model this article will show that the lack of variability in most of these datasets immediately leads to excellent models, even if that model is only one comparison per feature.

Video streaming systems aim to provide high-quality video adapted to clients’ device and network conditions. For this purpose, adaptive streaming architectures encode video content at a variety of quality levels, organized in a bitrate ladder. However, compressing a video into multiple streams is resource-intensive, which may become especially problematic in live streaming applications with real-time demands. Therefore, this paper proposes a novel solution for fast bitrate ladder creation, and provides the requirements for implementation in the H.266/VVC standard. More specifically, the proposed method creates new intermediate Combined Streams by injecting the lowest temporal layers of a higher-quality Augmentation Stream in a lower-quality Base Stream. Since the lowest layers are used as reference by the remaining layers, this procedure indirectly increases the quality of the frames in those untouched remaining layers as well. We demonstrate that injecting more layers brings both the quality and bitrate closer to that of the Augmentation Stream. The disadvantage of the Combined Streams is that their quality fluctuates more than the quality of the source streams, and that they are compressed less efficiently, comparable to going from a slower to fast or faster preset in the VVenC encoder. Most importantly, their main advantage is that they were generated at no significant additional computational complexity. In this way, the proposed method is of great benefit when generating a bitrate ladder of video streams under constrained computational resources.

Recent application domains such as the Internet of Things (IoT) and Smart Cities (SCs) have introduced novel challenges to Cloud Computing based on their stringent requirements (e.g., low latency, high bandwidth). With the exponential growth of IoT traffic in the last few years, traditional cloud systems have become inadequate for these applications since requests are made on-demand simultaneously by multiple devices at different locations. The Fog Computing (FC) paradigm has emerged to deal with the limitations of traditional clouds since computational resources are placed at the edges of the network, aiming to decrease the latency expected by IoT devices and reduce the amount of data sent to the cloud. However, research challenges persist in FC since it is not a mature concept yet. This PhD research addresses four challenges in the FC domain focused on providing an efficient resource allocation in these distributed infrastructures. This dissertation includes theoretical formulations as benchmarks for resource allocation, fog-based architectural concepts, anomaly detection practices for IoT, and latency-aware allocation approaches that lead to the implementation of a network-aware framework named Diktyo. It optimizes the allocation of container-based service chains by considering latency and bandwidth in the scheduling process of a well-known container orchestration platform, Kubernetes. Ex-periments showed thatDiktyo increases throughput by 22% and reduces latency by 45% for microservice benchmark applications.

Containers have revolutionized application deployment and life-cycle management in current cloud platforms. Applications have evolved from large monoliths to complex graphs of loosely-coupled microservices aiming to improve deployment flexibility and operational efficiency. However, modern microservice-based architectures are challenging since proper allocation and scaling of microservices is a difficult task due to their complex inter-dependencies. Existing works do not consider microservice dependencies, which could lead to the application’s performance degradation when service demand increases. This paper studies the impact of microservice interdependencies in auto-scaling mechanisms by proposing a novel framework named gym-hpa that enables different auto-scaling goals via Reinforcement Learning (RL). The framework has been developed based on the OpenAI Gym library for the popular Kubernetes (K8s) platform to bridge the gap between RL and auto-scaling research by training RL agents on real cloud environments. The aim is to improve resource usage and reduce the application’s response time in future cloud platforms by considering microservice inter-dependencies in horizontal scaling. Experiments with microservice benchmark applications show that RL agents trained with the gym-hpa framework can reduce on average resource usage by 30% and reduce the application’s response time by 25% compared to default scaling mechanisms.

Dynamic point cloud delivery can provide the required interactivity and realism to six degrees of freedom (6DoF) interactive applications. However, dynamic point cloud rendering imposes stringent requirements (e.g., frames per second (FPS) and quality) that current hardware cannot handle. A possible solution is to convert point cloud into meshes before rendering on the head-mounted display (HMD). However, this conversion can induce degradation in quality perception such as a change in depth, level of detail, or presence of artifacts. This paper, as one of the first, presents an extensive subjective study of the effects of converting point cloud to meshes with different quality representations. In addition, we provide a novel in-session content rating methodology, providing a more accurate assessment as well as avoiding post-study bias. Our study shows that both compression level and observation distance have their influence on subjective perception. However, the degree of influence is heavily entangled with the content and geometry at hand. Furthermore, we also noticed that while end users are clearly aware of quality switches, the influence on their quality perception is limited. As a result, this has the potential to open up possibilities in bringing the adaptive video streaming paradigm to the 6DoF environment.

In recent years, Virtual Reality (VR) has gained attention as a tool for a plethora of applications such as first-aid, firefighting and in the automotive industry. End-user immersion is a key factor in these applications to make the experience representative for its real-life counterpart. By enhancing the traditional audiovisual cues with additional sensory inputs in terms of haptic vibro-tactile and kinesthetic feedback, this immersion can be improved. But are current haptic implementations sufficient to provide the required added value? And how do they compare to other types of VR interaction? In this paper, we present a multi-modal VR training framework able to provide subjective and objective comparisons among three different interaction options: (i) haptic gloves, (ii) traditional VR controllers, and (iii) non-haptic handtracking. We performed a user test where the different interactivity flavours were compared in terms of their influence on both subjective perception and objective performance of the end-user by means of three VR training scenarios. The subjective results show an aversion towards non-haptic handtracking for constrained, cognitively light tasks while a preference towards controllers exist for more cognitively heavy multi-tasking. This is however not reflected in objective results, where differences between interaction methods are far less pronounced.

Intrusion Detection Systems (IDS) play an important role in detecting network intrusions. Because intrusions have many variants and zero-day attacks, traditional signature- and anomaly-based IDS often fail to detect them. On the other hand, solutions based on Machine Learning (ML), have better capabilities for detecting variants. In this work, we adopt an ML-based IDS which uses three in-sequence tasks, pre-processing, binary detection, and multi-class detection, with a multi-tier architecture with one-, two-, and three-tier architectural configurations. We then mapped three in-sequence tasks into these architectures, resulting in ten task assignments. We evaluated these with queueing theory to determine which tasks assignments were more appropriate for particular service providers. With simulated annealing, we obtained the computation capacity by allocating the total cost appropriate to each tier, based on the fixed parameter set with the objective of minimizing overall delay. These investigations showed that using only the edge and allocating all tasks to it gave the best performance. Furthermore, a two-tier architecture with edge and cloud components was also sufficient for IDS as a Service with the delay that was three times better than for other task assignments. Our results also indicate that more than 85% of the total capacity was allocated and spread across nodes in the lowest tier for pre-processing to reduce delays.

Containers have revolutionized application deployment and life-cycle management in current cloud platforms. Applications have evolved from single monoliths to complex graphs of loosely-coupled microservices. However, the efficient allocation of microservice-based applications is challenging due to their complex inter-dependencies. Further, recent applications are becoming even more delay-sensitive, demanding lower latency between dependent microservices. Scheduling policies in popular container orchestration platforms mainly aim to increase the resource efficiency of the infrastructure, insufficient for latency-sensitive applications. Application domains such as the Internet of Things and multi-tier Web services would benefit from network-aware policies that consider network latency and bandwidth in the scheduling process. Previous works have studied network-aware scheduling via theoretical formulations or heuristic-based methods evaluated via simulations or small testbeds, making their full applicability in popular platforms difficult. This paper proposes a novel network-aware framework for the popular Kubernetes (K8s) platform named Diktyo that determines the placement of dependent microservices in long-running applications focused on reducing the application’s end-to-end latency and guaranteeing bandwidth reservations. Simulations show that Diktyo can significantly reduce the network latency for various applications across different infrastructure topologies compared to default K8s scheduling plugins. Also, experiments in a K8s cluster with microservice benchmark applications show that Diktyo can increase database throughput by 22% and reduce application response time by 45%.

An intrusion detection system (IDS), traditionally an example of an effective security monitoring system, is facing significant challenges due to the ongoing digitization of our modern society. The growing number and variety of connected devices are not only causing a continuous emergence of new threats that are not recognized by existing systems, but the amount of data to be monitored is also exceeding the capabilities of a single system. This raises the need for a scalable IDS capable of detecting unknown, zero-day, attacks. In this paper, a novel multi-stage approach for hierarchical intrusion detection is proposed. The proposed approach is validated on the public benchmark datasets, CIC-IDS-2017 and CSE-CIC-IDS-2018. Results demonstrate that our proposed approach besides effective and robust zero-day detection, outperforms both the baseline and existing approaches, achieving high classification performance, up to 96% balanced accuracy. Additionally, the proposed approach is easily adaptable without any retraining and takes advantage of n-tier deployments to reduce bandwidth and computational requirements while preserving privacy constraints. The best-performing models with a balanced set of thresholds correctly classified 87% or 41 out of 47 zero-day attacks, while reducing the bandwidth requirements up to 69%.

Digital transformation is increasingly reliant onservice-based operations in fog networks. The latter is a geo-dispersed form of the cloud, extending resources closer to end-users for improved privacy and reduced latency. The dispersion leverages diversity of compute-network capacities and energy prices, while promotes the coexistence of multiple providers. This drives variation in operational cost, coupled with limited information sharing across providers. Consequently, there is a critical need for an orchestration solution that preserves autonomy and optimizes operational cost across domains, while meeting service requirements. This paper proposes a novel service-based fog management and network orchestrator (sbMANO), which utilizes service metadata in enabling multi-provider resource management. The sbMANO is empowered with a novel optimization algorithm for service-based joint request mapping and response routing. The algorithm acts on partial information and preserves the edge for delay-critical services. The performance of the algorithm is evaluated analytically fordelay-awareanddelay-agnosticvariants. The results show that both achieve near-optimal performance in maximizing user satisfaction with minimum operational cost. Furthermore, the delay-aware variant outperforms the agnostic counterpart, with higher user satisfaction and lower operational cost.

In recent years, the continuing growth of the network edge, along with increasing user demands, has led to the need for increasingly complex and responsive management strategies for edge services. Many of these strategies are cloud-based, offering near-perfect solutions at the cost of requiring massive computational power, or edge-based, offering reactive strategies to changing edge conditions. This paper presents a decentralized, pro-active Quality of Experience (QoE) based architecture designed to run on edge nodes, which allows nodes to predict optimal service providers (fog nodes) in advance and request their services. The concepts behind the components of the architecture are explained, as well as geometry-inspired design decisions to limit model size. Evaluations on an NVIDIA Jetson Nano show that the architecture can predict optimal service providers for an edge node in real-time for 5 to 20 QoS (Quality of Service) and QoE parameters, with at least 50 potential fog nodes, and that overall QoE resulting from its use is improved by 1% to 18% over previous work such as SoSwirly, depending on the scenario.

Point cloud video streaming is a fundamental application of immersive multimedia. In it, objects represented as sets of points are streamed and displayed to remote users. Given the high bandwidth requirements of this content, small changes in the network and/or encoding can affect the users' perceived quality in unexpected manners. To tackle the degradation of the service as fast as possible, real-time Quality of Experience (QoE) assessment is needed. As subjective evaluations are not feasible in real time due to their inherent costs and duration, low-complexity objective quality assessment is a must. Traditional No-Reference (NR) objective metrics at client side are best suited to fulfill the task. However, they lack on accuracy to human perception. In this paper, we present a cluster-based objective NR QoE assessment model for point cloud video. By means of Machine Learning (ML)-based clustering and prediction techniques combined with NR pixel-based features (e.g., blur and noise), the model shows high correlations (up to a 0.977 Pearson Linear Correlation Coefficient (PLCC)) and low Root Mean Squared Error (RMSE) (down to 0.077 on a zero-to-one scale) towards objective benchmarks after evaluation on an adaptive streaming point cloud dataset consisting of sixteen source videos and 453 sequences in total.

Most newly proposed detection methods in intrusion detection incorporate machine learning models to distinguish between benign and malicious traffic. The models are validated on a handful of academic datasets and ranked based on their classification performance. This article aims to demonstrate that unbeknownst to the new models' authors, there are features in these datasets which heavily bias the results and obscure a realistic, reliable estimate of the separability of the datasets. This paper proposes a methodology to estimate the contaminating influence of a dataset’s features based on the concept of blind generalization. The novel methodology is subsequently used to assess the features of six widely adopted intrusion detection datasets. In each dataset, several features show a pattern where regardless of training attack class, the models blindly generalize towards all available attack classes with nearly identical classification metrics. These features provide undeserved boosts in the baseline classification scores for each dataset. By themselves, some contaminant features even push these baselines upwards of 90% accuracy (balanced).

Abstract Nowadays, many complex multi‐vendor production environments, such as telecom infrastructures in smart cities or on‐board passenger information systems in trains, are based on micro‐services and deployed in the cloud. From a service integrator viewpoint, building new solutions for these environments, which can host a large number of externally designed and developed micro‐services, is often complex and error‐prone. This is in part due to undocumented behaviour or undocumented architectural specifications of such systems. Advanced service monitoring can offer a solution to quickly detect anomalies or unexpected service interaction behaviour during on‐site integration. However, the monitoring service should not have an impact on the production environment itself. Therefore, this article proposes an agent‐based unobtrusive monitoring platform, capable of monitoring both internally developed and externally developed services through the use of sidecar containers. It monitors state, metrics and network traffic at micro‐service level and the research was conducted as part of the DynAMo research project, a collaboration with various industry partners. Prototype evaluation proves that our solution has a negligible impact (below 0.02% CPU usage on average) on an existing micro‐service environment just as other monitoring systems like Prometheus while offering additional functionality focused on multi‐vendor service integration. This makes it suitable to be deployed in complex production domains to further aid on‐site integration and quickly find potential new anomalies.

In recent years, containers have gained popularity as a lightweight virtualization technology. This rise in popularity has gone hand in hand with the adoption of microservice architectures, mostly thanks to the scalable, ethereal, and isolated nature of containers. More recently, edge devices have become powerful enough to be able to run containerized microservices, while remaining flexible enough in terms of size and power to be deployed almost anywhere. This has triggered research into several container placement strategies involving edge networks, leading to concepts such as osmotic computing. While these container placement strategies are optimal in terms of workload placement, current container orchestrators are often not suitable for running on edge devices due to their high resource requirements. In this article, FLEDGE is presented as a Kubernetes-compatible container orchestrator based on Virtual Kubelets, aimed primarily at container orchestration on low-resource edge devices. Several aspects of low-resource container orchestration are examined, such as the choice of container runtime and how to realize container networking. A number of evaluations are performed to determine how FLEDGE compares to Kubernetes and K3S in terms of resource requirements, showing that it needs around 60MiB memory and 78MiB storage to run on a Raspberry Pi 3, including all dependencies, which is significantly less than both studied alternatives.

Accurate and fast estimations or predictions of the (near) future location of the users of head-mounted devices within the virtual omnidirectional environment open a plethora of opportunities in application domains such as interactive immersive gaming and tele-surgery. Therefore, the past years have seen growing attention to models for viewport prediction in 360֯ environments. Among the approaches, content-agnostic, trajectory-based methods have the potential to provide very fast solutions, as they do not require complex analysis of the videos to provide a prediction. However, accurate trajectory-based viewport prediction is rather difficult due to the intrinsic variability in user behaviour. Furthermore, even when making use of machine learning, current approaches tend to be brute-force and heavily tailored to specific datasets with little comparison to existing benchmarks or publicly available studies. This article presents a generic, content-agnostic viewport prediction method consisting of a window-based approach combined with a preprocessing system to classify behavioural patterns in terms of user clustering and trajectory correlation. Moreover, as the state of the art does not provide a comparative analysis of different approaches, this work contributes to this. Based on the obtained results, a combined prediction model is proposed and evaluated. Our method shows a 36.8% to 53.9% improvement when compared to the static prediction baseline for a prediction horizon of 8 seconds. In addition, a 11.5% to 24.0% improvement to a brute-force machine learning prediction approach is obtained. As such, this work contributes towards the creation of more generic and structured solutions for content-agnostic viewport prediction in terms of data representation, preprocessing and modelling.

Immersive media services, such as Augmented and Virtual Reality (AR/VR) are getting significant attention in recent years with the promise of bringing immersive experiences to end users. However, despite the remarkable advances in the field, AR/VR applications are mostly local and individual experiences. The main obstacle between current technology and future remote, multi-user AR/VR applications is the stringent end-to-end (E2E) latency requirement, which cannot exceed 20 ms to avoid motion sickness. Emerging AR/VR services put even more pressure on current network infrastructures, calling for considerable advancements toward fully cloud-native architectures. Cloud-based VR services, where participants can virtually interact across vast distances, remain a distant dream. Several challenges still arise concerning the deployment and management of VR services. This paper presents a Mixed-Integer Linear Programming (MILP) formulation for the efficient orchestration of VR services in fog-cloud infrastructures. The model considers Fog Computing (FC), an extension of cloud computing, and Segment Routing (SR), which leverages the source routing paradigm. The evaluation of realistic VR container-based service chains shows that deploying VR components hosted in a fog-cloud infrastructure can satisfy the 20 ms latency boundary.

In recent years, computing workloads have shifted from the cloud to the fog and edge, as IoT devices are becoming powerful enough to run containerized services. While the fog and edge computing can increase energy efficiency, reduce network traffic and provide better end user experience, the scale and volatility of the fog and edge also present new problems for service scheduling. In the edge, there are orders of magnitude more devices than in cloud data centers, and conditions are often less stable. Additionally, unlike in data centers, the network topology of the edge often changes, requiring a real-time approach to scheduling. In this demonstration, an implementation of a highly scalable orchestrator named “Swirly” is presented. The challenge of fog service scheduling is illustrated by using this implementation to organize software services in near real-time and on-demand in a virtual representation of a real-world industry park. Performance indicators are presented to show that this solution can scale up to 300.000 edge nodes.

Volumetric media streaming will be one of the fundamental technologies to enable near future immersive multimedia experiences. In it, objects represented as sets of points (i.e. point-clouds), are presented to remote users wearing Head-Mounted Displays (HMDs). Due to the stringent bandwidth and latency requirements of such applications, small changes in the network can affect the user in unexpected manners (physical discomfort, lack of concentration, etc.). Therefore, there is a need for assessing the perceived quality of this type of applications in real-time, i.e, the Quality of Experience (QoE). Given that subjective evaluations are not feasible for (near) real-time applications, objectively measuring this quality will be a must. While traditional objective metrics could potentially be used to fulfill the task, it is still unclear how accurate they are to assess volumetric media. To this end, this paper presents a thorough correlation analysis of both Full Reference (FR) and No Reference (NR) objective metrics to subjective Mean Opinion Scores (MOS) for different volumetric streaming scenarios. To enhance the accuracy, multiple Region-Of-Interest (ROI) selection and weighting procedures have been applied and their influence on the results have been investigated. Our results show that the classical video quality metric Video Multimethod Assessment Fusion (VMAF) is well-suited as an objective benchmark for volumetric media streaming in terms of correlation to subjective scores, while a combination of NR features could provide a suitable real-time assessment. Finally, ROI selection proves to widen the range of objective metrics, which is an important issue to apply traditional objective metrics to volumetric media.

Anomalies and faults can be detected, and their causes verified, using both data-driven and knowledge-driven techniques. Data-driven techniques can adapt their internal functioning based on the raw input data but fail to explain the manifestation of any detection. Knowledge-driven techniques inherently deliver the cause of the faults that were detected but require too much human effort to set up. In this paper, we introduce FLAGS, the Fused-AI interpretabLe Anomaly Generation System, and combine both techniques in one methodology to overcome their limitations and optimize them based on limited user feedback. Semantic knowledge is incorporated in a machine learning technique to enhance expressivity. At the same time, feedback about the faults and anomalies that occurred is provided as input to increase adaptiveness using semantic rule mining methods. This new methodology is evaluated on a predictive maintenance case for trains. We show that our method reduces their downtime and provides more insight into frequently occurring problems.

Abstract Internet of Things (IoT) technologies, when adequately integrated, cater for logistics optimisation and operations' environmental impact monitoring, both key aspects for today's EU ports management. This article presents Obelisk, a scalable and multi‐tenant cloud‐based IoT integration platform used in the EU H2020 PortForward project. The landscape of IoT protocols being particularly fragmented, the first role of Obelisk is to provide uniform access to data originating from a myriad of devices and protocols. Interoperability is achieved through adapters that provide flexibility and evolvability in protocol and format mapping. Additionally, due to ports operating in a hub model with various interacting actors, a second role of Obelisk is to secure access to data. This is achieved through encryption and isolation for data transport and processing, respectively, while user access control is ensured through authentication and authorisation standards. Finally, as ports IoTisation will further evolve, a third need for Obelisk is to scale with the data volumes it must ingest and process. Platform scalability is achieved by means of a reactive micro‐services based design. Those three essential characteristics are detailed in this article with a specific focus on how to achieve IoT data platform scalability. By means of an air quality monitoring use‐case deployed in the city of Antwerp, the scalability of the platform is evaluated. The evaluation shows that the proposed reactive micro‐service based design allows for horizontal scaling of the platform as well as for logarithmic time complexity of its service time.

The success and quality of this journal depends on the dedication and expertise of a large number of reviewers. On behalf of the Editorial Board, I would like to thank them explicitly for their excellent work during the non-trivial year 2020. Their substantial and constructive reviews have proven to be very valuable to the authors, and are highly appreciated.

Service Function Chaining and Network Function Virtualization are enabling technologies that provide dynamic network services with diverse QoS requirements. Regarding the limited infrastructure resources, service providers need to prioritize service requests and even reject some of low-priority requests to satisfy the requirements of high-priority services. In this paper, we study the problem of deployment and reconfiguration of a set of chains with different priorities with the objective of maximizing the service provider's profit; wherein, we also consider management concerns including the ability to control the migration of virtual functions. We show the problem is more practical and comprehensive than the previous studies, and propose an MILP formulation of it along with two solving algorithms. The first algorithm is a fast polynomial-time heuristic that calculates an initial feasible solution to the problem. The second algorithm is an exact method that utilizes the initial feasible solution to achieve the optimal solution quickly. Using extensive simulations, we evaluate the algorithms and show the proposed heuristic can find a feasible solution in at least 83% of the simulation runs in less than 7 seconds, and the exact algorithm can achieve 25% more profit 8 times faster than the state-of-the-art MILP solving methods.

Recent trends have caused a shift from services deployed solely in monolithic data centers in the cloud to services deployed in the fog (e.g. roadside units for smart highways, support services for IoT devices). Simultaneously, the variety and number of IoT devices has grown rapidly, along with their reliance on cloud services. Additionally, many of these devices are now themselves capable of running containers, allowing them to execute some services previously deployed in the fog. The combination of IoT devices and fog computing has many advantages in terms of efficiency and user experience, but the scale, volatile topology and heterogeneous network conditions of the fog and the edge also present problems for service deployment scheduling. Cloud service scheduling often takes a wide array of parameters into account to calculate optimal solutions. However, the algorithms used are not generally capable of handling the scale and volatility of the fog. This paper presents a scheduling algorithm, named "Swirly", for large scale fog and edge networks, which is capable of adapting to changes in network conditions and connected devices. The algorithm details are presented and implemented as a service using the Kubernetes API. This implementation is validated and benchmarked, showing that a single threaded Swirly service is easily capable of managing service meshes for at least 300.000 devices in soft real-time.

Fog computing is a paradigm that extends cloud computing services to the edge of the network in order to support delay-sensitive Internet of Things (IoT) services. One of the most promising use-cases of fog computing is Smart City scenarios. Fog computing can substantially improve the quality of citywide services by reducing response delays. Owing to geographically distributed and resource-constrained fog nodes and a multitude of IoT devices in Smart Cities, efficient service deployment and end device traffic routing are quite challenging. Therefore, in this paper, we present an Integer Linear Programming (ILP) formulation for the Joint Application Component Placement and Traffic Routing (JAcPTR) problem in which users' delay requirements and the limited traffic processing capacity of application instances are considered. Besides, the JAcPTR enables users and infrastructure managers to easily enforce their locality and management requirements in the deployment of application instances. To cope with the considerably high execution time in large instances of the JAcPTR problem, we propose a fast polynomial-time heuristic to efficiently solve the problem. The performance of the proposed heuristic has been evaluated through extensive simulation. Results show that in large instances of the problem, while the state-of-the-art Mixed Integer Linear Programming (MILP) solver fails to obtain a solution in 50% of the simulation runs in 300 seconds, our proposed heuristic can obtain a near-optimal solution in less than one second.

In recent years, cloud computing is evolving towards a distributed paradigm called Fog Computing, aiming to provide a distributed infrastructure by placing computational resources close to end-users. To fully leverage on Fog Computing, proper resource allocation is needed to cope with the demanding constraints introduced by IoT (e.g. low latency, high mobility). One of the main challenges that remain is Service Function Chaining (SFC). Services must be connected in a specific order forming an SFC allowing providers to benefit from the high flexibility and low operational costs introduced by network softwarization. In the demonstration, an SFC controller able to optimize the placement of service chains in Fog-cloud environments will be presented. The SFC controller has been implemented on the Kubernetes platform, an open-source orchestrator for the automatic deployment of micro-services. Our approach allows Kubernetes to deploy micro-services based on up-to-date information on the current status of the network infrastructure. The demonstration will show how application developers could use our approach to set up service chains for their services. Then, performance outcomes of our SFC controller will be shown, especially in terms of container deployment times.

Augmented Reality (AR) and Virtual Reality (VR) multimodal systems are the latest trend within the field of multimedia. As they emulate the senses by means of omnidirectional visuals, 360° sound, motion tracking and touch simulation, they are able to create a strong feeling of presence and interaction with the virtual environment. These experiences can be applied for virtual training (Industry 4.0), tele-surgery (healthcare) or remote learning (education). However, given the strong time and task sensitiveness of these applications, it is of great importance to sustain the end-user quality, i.e. the Quality-of-Experience (QoE), at all times. Lack of synchronization and quality degradation need to be reduced to a minimum to avoid feelings of cybersickness or loss of immersiveness and concentration. This means that there is a need to shift the quality management from system-centered performance metrics towards a more human, QoE-centered approach. However, this requires for novel techniques in the three areas of the QoE-management loop (monitoring, modelling and control). This position paper identifies open areas of research to fully enable human-centric driven management of immersive multimedia. To this extent, four main dimensions are put forward: (1) Task and well-being driven subjective assessment; (2) Real-time QoE modelling; (3) Accurate viewport prediction; (4) Machine Learning (ML)-based quality optimization and content recreation. This paper discusses the state-of-the-art, and provides with possible solutions to tackle the open challenges.

Recently, the fifth-generation mobile network (5G) is getting significant attention. Empowered by Network Function Virtualization (NFV), 5G networks aim to support diverse services coming from different business verticals (e.g. Smart Cities, Automotive, etc). To fully leverage on NFV, services must be connected in a specific order forming a Service Function Chain (SFC). SFCs allow mobile operators to benefit from the high flexibility and low operational costs introduced by network softwarization. Additionally, Cloud computing is evolving towards a distributed paradigm called Fog Computing, which aims to provide a distributed cloud infrastructure by placing computational resources close to end-users. However, most SFC research only focuses on Multi-access Edge Computing (MEC) use cases where mobile operators aim to deploy services close to end-users. Bi-directional communication between Edges and Cloud are not considered in MEC, which in contrast is highly important in a Fog environment as in distributed anomaly detection services. Therefore, in this paper, we propose an SFC controller to optimize the placement of service chains in Fog environments, specifically tailored for Smart City use cases. Our approach has been validated on the Kubernetes platform, an open-source orchestrator for the automatic deployment of micro-services. Our SFC controller has been implemented as an extension to the scheduling features available in Kubernetes, enabling the efficient provisioning of container-based SFCs while optimizing resource allocation and reducing the end-to-end (E2E) latency. Results show that the proposed approach can lower the network latency up to 18% for the studied use case while conserving bandwidth when compared to the default scheduling mechanism.

Ranging from traditional video streaming to Virtual Reality (VR) videos, the demand for video applications to mobile devices is booming. In the context of mobile operators a challenging problem is how to handle the increasing video traffic while managing the interplay between infrastructure optimization and QoE. Solving this issue is remarkably difficult, and recent investigations do not consider large-scale networks. In this dissertation paper we explore the solution space of efficient video streaming over mobile networks. First, we propose a model to predict video streaming quality based on the observation of performance indicators of the underlying IP network. Second, we introduce a novel QoE-aware path deployment heuristic for large-scale SDN-based mobile networks. Third, based on the lessons learned with QoE prediction for traditional video streaming, we finally explore the VR video domain by proposing PERCEIVE and VR-EXP. PERCEIVE is a two-stage method for predicting the perceived quality of adaptive VR videos when streamed through mobile networks. In turn, VR-EXP consists of an experimentation platform that allows in-depth evaluation of state-of-the-art VR video optimization techniques. Obtained results show that the combination of the proposed methods for QoE-aware path selection outperformed state-of-the-art approaches.

Volumetric media has the potential to provide the six degrees of freedom (6DoF) required by truly immersive media. However, achieving 6DoF requires ultra-high bandwidth transmissions, which real-world wide area networks cannot provide today. Therefore, recent efforts have started to target efficient delivery of volumetric media, using a combination of compression and adaptive streaming techniques. It remains, however, unclear how the effects of such techniques on the user perceived quality can be accurately evaluated. In this paper, we present the results of an extensive objective and subjective quality of experience (QoE) evaluation of volumetric 6DoF streaming. We use PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple dynamic point cloud objects. By means of a thorough analysis, we investigate the perceived quality impact of the available bandwidth, rate adaptation algorithm, viewport prediction strategy and user's motion within the scene. We determine which of these aspects has more impact on the user's QoE, and to what extent subjective and objective assessments are aligned.

This paper contributes to the pursuit of leveraging unstructured medical notes to structured clinical decision making. In particular, we present a pipeline for clinical information extraction from medical notes related to preterm birth, and discuss the main challenges as well as its potential for clinical practice. A large collection of medical notes, created by staff during hospitalizations of patients who were at risk of delivering preterm, was gathered and analyzed. Based on an annotated collection of notes, we trained and evaluated information extraction components to discover clinical entities such as symptoms, events, anatomical sites and procedures, as well as attributes linked to these clinical entities. In a retrospective study, we show that these are highly informative for clinical decision support models that are trained to predict whether delivery is likely to occur within specific time windows, in combination with structured information from electronic health records.

The success and quality of this journal depends on the dedication and expertise of a large number of reviewers. On behalf of the Editorial Board, I would like to thank them explicitly for their excellent work. Their substantial and constructive reviews have proven to be very valuable to the authors, and are highly appreciated.

To cope with the massive bandwidth demands of Virtual Reality (VR) video streaming, both the scientific community and the industry have been proposing optimization techniques such as viewport-aware streaming and tile-based adaptive bitrate heuristics. As most of the VR video traffic is expected to be delivered through mobile networks, a major problem arises: both the network performance and VR video optimization techniques have the potential to influence the video playout performance and the Quality of Experience (QoE). However, the interplay between them is neither trivial nor has it been properly investigated. To bridge this gap, in this article, we introduce VR-EXP, an open-source platform for carrying out VR video streaming performance evaluation. Furthermore, we consolidate a set of relevant VR video streaming techniques and evaluate them under variable network conditions, contributing to an in-depth understanding of what to expect when different combinations are employed. To the best of our knowledge, this is the first work to propose a systematic approach, accompanied by a software toolkit, which allows one to compare different optimization techniques under the same circumstances. Extensive evaluations carried out using realistic datasets demonstrate that VR-EXP is instrumental in providing valuable insights regarding the interplay between network performance and VR video streaming optimization techniques.

The increasing popularity of head-mounted devices and 360° video cameras allows content providers to provide virtual reality (VR) video streaming over the Internet, using a two-dimensional representation of the immersive content combined with traditional HTTP adaptive streaming (HAS) techniques. However, since only a limited part of the video (i.e., the viewport) is watched by the user, the available bandwidth is not optimally used. Recent studies have shown the benefits of adaptive tile-based video streaming; rather than sending the whole 360° video at once, the video is cut into temporal segments and spatial tiles, each of which can be requested at a different quality level. This allows prioritization of viewable video content and thus results in an increased bandwidth utilization. Given the early stages of research, there are still a number of open challenges to unlock the full potential of adaptive tile-based VR streaming. The aim of this work is to provide an answer to several of these open research questions. Among others, we propose two tile-based rate adaptation heuristics for equirectangular VR video, which use the great-circle distance between the viewport center and the center of each of the tiles to decide upon the most appropriate quality representation. We also introduce a feedback loop in the quality decision process, which allows the client to revise prior decisions based on more recent information on the viewport location. Furthermore, we investigate the benefits of parallel TCP connections and the use of HTTP/2 as an application layer optimization. Through an extensive evaluation, we show that the proposed optimizations result in a significant improvement in terms of video quality (more than twice the time spent on the highest quality layer), compared to non-tiled HAS solutions.

The increased reliance of data management applications on cloud computing technologies has rendered research in identifying solutions to the data placement problem to be of paramount importance. The objective of the classical data placement problem is to optimally partition, while also allowing for replication, the set of data-items into distributed data centers to minimize the overall network communication cost. Despite significant advancement in data placement research, replica placement has seldom been studied in unison with data placement. More specifically, most of the existing solutions employ a two-phase approach: 1) data placement, followed by 2) replication. Replication should however be seen as an integral part of data placement, and should be studied as a joint optimization problem with the latter. In this paper, we propose a unified paradigm of data placement, called CPR, which combines data placement and replication of data-intensive services into geographically distributed clouds as a joint optimization problem. Underneath CPR, lies an overlapping correlation clustering algorithm capable of assigning a data-item to multiple data centers, thereby enabling us to jointly solve data placement and replication. Experiments on a real-world trace-based online social network dataset show that CPR is effective and scalable. Empirically, it is approximate to 35% better in efficacy on the evaluated metrics, while being up to 8 times faster in execution time when compared to state-of-the-art techniques.

In crisis situations, it is important to be able to quickly gather information from various sources to form a complete and accurate picture of the situation. However, the different policies of participating companies often make it difficult to connect their information sources quickly, or to allow software to be deployed on their networks in a uniform way. The difficulty in deploying software is exacerbated by the fact that companies often use different software platforms in their existing networks. In this paper, Flexible federated Unified Service Environment (FUSE) is presented as a solution for joining multiple domains into a microservice based ad hoc federation, and for deploying and managing container-based software on the devices of a federation. The resource requirements for setting up a FUSE federation are examined, and a video streaming application is deployed to demonstrate the performance of software deployed on an example federation. The results show that FUSE can be deployed in 10 minutes or less, and that it can support multiple video streams under normal network conditions, making it a viable solution for the problem of quick and easy cross-domain federation.

For years, containers have been a popular choice for lightweight virtualization in the cloud. With the rise of more powerful and flexible edge devices, container deployment strategies have arisen that leverage the computational power of edge devices for optimal workload distribution. This move from a secure data center network to heterogenous public and private networks presents some issues in terms of security and network topology that can be partially solved by using a Virtual Private Network (VPN) to connect edge nodes to the cloud. In this paper, the scalability of VPN software is evaluated to determine if and how it can be used in large-scale clusters containing edge nodes. Benchmarks are performed to determine the maximum number of VPN-connected nodes and the influence of network degradation on VPN performance, primarily using traffic typical for edge devices generating IoT data. Some high level conclusions are drawn from the results, indicating that WireGuard is an excellent choice of VPN software to connect edge nodes in a cluster. Analysis of the results also shows the strengths and weaknesses of other VPN software.

Emergency services must be able to transfer data with high priority over different networks. With 5G, slicing concepts at mobile network connections are introduced, allowing operators to divide portions of their network for specific use cases. In addition, Software-Defined Networking (SDN) principles allow to assign different Quality-of-Service (QoS) levels to different network slices.This paper proposes an SDN-based solution, executable both offline and online, that guarantees the required bandwidth for the emergency flows and maximizes the best-effort flows over the remaining bandwidth based on their priority. The offline model allows to optimize the problem for a batch of flow requests, but is computationally expensive, especially the variant where flows can be split up over parallel paths. For practical, dynamic situations, an online approach is proposed that periodically recalculates the optimal solution for all requested flows, while using shortest path routing and a greedy heuristic for bandwidth allocation for the intermediate flows.Afterwards, the offline approaches are evaluated through simulations while the online approach is validated through physical experiments with SDN switches, both in a scenario with 500 best-effort and 50 emergency flows. The results show that the offline algorithm is able to guarantee the resource allocation for the emergency flows while optimizing the best-effort flows with a sub-second execution time. As a proof-of-concept, a physical setup with Zodiac switches effectively validates the feasibility of the online approach in a realistic setup.

Drones and thermal cameras are often combined within applications such as search and rescue, and fire fighting.Due to vendor specific hardware and software, applications for these drones are hard to develop and maintain.As a result, a pluggable drone imaging analysis architecture is proposed that facilitates the development of custom image processing applications.This architecture is prototyped as a microservice-based plugin framework and allows users to build image processing applications by connecting media streams using microservices that connect inputs (e.g.regular or thermal camera image streams) to image analysis services.The prototype framework is evaluated in terms of modifiability, interoperability and performance.This evaluation has been carried out on the use case of detecting large crowds of people (mobs) during open-air events.The framework achieves modifiability and performance by being able to work in soft real-time and it achieves the interoperability by having an average successful exchange ratio of 99.998%.A new dataset containing thermal images of such mobs is presented, on which a YOLOv3 neural network is trained.The trained model is able to detect mobs on new thermal images in real-time achieving frame rates of 55 frames per second when deployed on a modern GPU.

This paper describes the process and results of analyzing CICIDS2017, a modern, labeled data set for testing intrusion detection systems. The data set is divided into several days, each pertaining to different attack classes (Dos, DDoS, infiltration, botnet, etc.). A pipeline has been created that includes nine supervised learning algorithms. The goal was binary classification of benign versus attack traffic. Cross-validated parameter optimization, using a voting mechanism that includes five classification metrics, was employed to select optimal parameters. These results were interpreted to discover whether certain parameter choices were dominant for most (or all) of the attack classes. Ultimately, every algorithm was retested with optimal parameters to obtain the final classification scores. During the review of these results, execution time, both on consumerand corporate-grade equipment, was taken into account as an additional requirement. The work detailed in this paper establishes a novel supervised machine learning performance baseline for CICIDS2017.

Small-to-medium businesses are increasingly relying on big data platforms to run their analytical workloads in a cost-effective manner, instead of using conventional and costly data warehouse systems. However, the distributed nature of big data technologies makes it time-consuming to process typical analytical queries, especially those involving aggregate and join operations, preventing business users from performing efficient data exploration. In this sense, a workload-driven approach for automatic view selection was devised, aimed at speeding up analytical queries issued against distributed dimensional data. This paper presents a detailed description of the proposed approach, along with an extensive evaluation to test its feasibility. Experimental results shows that the conceived mechanism is able to automatically derive a limited but comprehensive set of views able to reduce query processing time by up to 89%-98%.

The increasing popularity of head-mounted devices and 360° video cameras allows content providers to offer virtual reality video streaming over the Internet, using a relevant representation of the immersive content combined with traditional streaming techniques. While this approach allows the user to freely move her head, her location is fixed by the camera's position within the scene. Recently, an increased interest has been shown for free movement within immersive scenes, referred to as six degrees of freedom. One way to realize this is by capturing objects through a number of cameras positioned in different angles, and creating a point cloud which consists of the location and RGB color of a significant number of points in the three-dimensional space. Although the concept of point clouds has been around for over two decades, it recently received increased attention by ISO/IEC MPEG, issuing a call for proposals for point cloud compression. As a result, dynamic point cloud objects can now be compressed to bit rates in the order of 3 to 55 Mb/s, allowing feasible delivery over today's mobile networks. In this paper, we propose PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple, dynamic point cloud objects. We present a number of rate adaptation heuristics which use information on the user's position and focus, the available bandwidth, and the client's buffer status to decide upon the most appropriate quality representation of each object. Through an extensive evaluation, we discuss the advantages and drawbacks of each solution. We argue that the optimal solution depends on the considered scene and camera path, which opens interesting possibilities for future work.

Adapting and tiling the streaming of virtual reality (VR) video content has the potential to reduce the ultra-high bandwidth requirements of this type of multimedia services. Towards that goal, the optimization of a number of aspects is currently actively being researched. Novel rate adaptation heuristics, sophisticated viewport prediction algorithms and streaming protocol optimizations have proven their value to improve certain aspect of the VR streaming chain. However, the interplay between all these different optimizations as well as their tradeoff has not yet been explored in an experimental playground. The purpose of this demonstrator is to provide a full end-to-end adaptive tile-based VR video streaming system where each of the optimization aspects can be tuned with and their effect illustrated on-site.

Nowadays, the Internet of Things (IoT) continues to expand at enormous rates. Smart Cities powered by connected sensors promise to transform public services from transportation to environmental monitoring and healthcare to improve citizen welfare. Furthermore, over the last few years, Fog Computing has been introduced to provide an answer to the massive growth of heterogeneous devices connected to the network. Nevertheless, providing a proper resource scheduling for delay-sensitive and data-intensive services in Fog Computing environments is still a key research domain. Therefore, in this paper, a network-aware scheduling approach for container-based applications in Smart City deployments is proposed. Our proposal has been validated on the Kubernetes platform, an open source orchestrator for the automatic management and deployment of micro-services. Our approach has been implemented as an extension to the default scheduling mechanism available in Kubernetes, enabling Kubernetes to make resource provisioning decisions based on the current status of the network infrastructure. Evaluations based on Smart City container-based applications have been carried out to compare the performance of the proposed scheduling approach with the standard scheduling feature available in Kubernetes. Results show that the proposed approach achieves reductions of 80% in terms of network latency when compared to the default scheduling mechanism.

Next generation application domains such as Virtual Reality (VR), Augmented Reality (AR) together with the Tactile Internet paradigm impose ultra-low latency requirements on the networks (1 to 5 ms end-to-end latency). Towards this objective, networks are undergoing a tremendous transformation from the current packet switching models to Software Defined Networking (SDN) architectures, which provide programmability to configure the network. In its simplest variant, one single centralized controller orchestrates the whole SDN infrastructure. However, the fully centralized architecture (one single controller) can become a performance bottleneck, especially in terms of response throughput and flow setup latency. Furthermore, it suffers from massive scalability issues. In this direction, a number of more sophisticated SDN architectures are currently under research. While their theoretical advantages have been thoroughly discussed in the state-of-the-art, a comparative experimental analysis of these architectures is still missing. This study aims at providing such experimental performance comparison. Herein, we put to test a set of SDN architectures ranging from a fully centralized to a completely distributed control plane, comparing them in terms of flow setup latency. Overall results show that completely distributed architectures provide significantly better performance with almost 31% gain in terms of average flow setup latency over the centralized case.

Virtual Reality (VR) has the potential to change not only to the way we consume and perceive entertainment but also to improve other important areas of society. One sector that is starting to benefit from the advantages of VR is the treatment of stress related mental illnesses. VR is able to bring relaxation therapy to the next level in which solutions can be scalable (without the need for real-time dedicated professionals) and personalized. This paper presents VRelax, a personalized VR relaxation therapy approach. By means of semantic methodologies and online learning techniques, VRelax provides a personalized, relaxing virtual environment to the user.

Accurately predicting where the user of a Virtual Reality (VR) application will be looking at in the near future improves the perceive quality of services, such as adaptive tile-based streaming or personalized online training. However, because of the unpredictability and dissimilarity of user behavior it is still a big challenge. In this work, we propose to use reinforcement learning, in particular contextual bandits, to solve this problem. The proposed solution tackles the prediction in two stages: (1) detection of movement; (2) prediction of direction. In order to prove its potential for VR services, the method was deployed on an adaptive tile-based VR streaming testbed, for benchmarking against a 3D trajectory extrapolation approach. Our results showed a significant improvement in terms of prediction error compared to the benchmark. This reduced prediction error also resulted in an enhancement on the perceived video quality.

The continuous financial pressure on hospitals forces them to rethink various workflows. We focus on optimizing hospital transports, within the hospital, as they count up to 30% of the overall hospital cost. In this paper, we discuss a self-learning platform that learns the causes of transport delays, in order to avoid these kinds of delays in the future. We pay special attention to the explainability of the self-learning system, such that management understands the learned causes and remains in control over the automated process. This is achieved by providing the learned causes as sentences that can be understood by non-technical personnel and allowing these causes to first be supervised before the system takes them into account. Once approved, the system will calculate how much more time should be assigned to these transports in order to avoid future delays. As a result, the scheduling of patient transportation can be automatically optimized, while management remains in full control of the process.

Internet of Things (IoT)-based solutions for sport analytics aim to improve performance, coaching, and strategic insights. These factors are especially relevant in cycling, where real-time data should be available anytime, anywhere, even in remote areas where there are no infrastructure-based communication technologies (e.g., LTE and Wi-Fi). In this article, we present an experience report on the use of state-of-the-art IoT technologies in cycling, where a group of cyclists can form a reliable and energy efficient mesh network to collect and process sensor data in real-time, such as heart rate, speed, and location. This data is analyzed in real-time to estimate the performance of each rider and derive instantaneous feedback. Our solution is the first to combine a local body area network to gather the sensor data from the cyclist and a 6TiSCH network to form a multihop long-range wireless sensor network in order to provide each bicycle with connectivity to the sink (e.g., a moving car following the cyclists). In this article, we present a detailed technical description of this solution, describing its requirements, options, and technical challenges. In order to assess such a deployment, we present a large publicly available data-set from different real-world cycling scenarios (mountain road cycle racing and cyclo-cross) which characterizes the performance of the approach, demonstrating its feasibility and evidencing its relevance and promising possibilities in a cycling context for providing low-power communication with reliable performance.

Summary Within the context of cloud computing, efficient resource management is of great importance as it can result in higher scalability and significant energy and cost reductions over time. Because of the high complexity and costs of cloud environments, however, newly developed resource allocation strategies are often only validated by means of simulations, for example, by using CloudSim or custom‐developed simulation tools. This article describes a general approach for the validation of cloud resource allocation strategies, illustrating the importance of experimental validation on physical testbeds. Furthermore, the design and implementation of Raspberry Pi as a Service (RPiaaS), a low‐cost embedded testbed built using Raspberry Pi nodes, is presented. RPiaaS aims to facilitate the step from simulations toward experimental evaluations on larger cloud testbeds and is designed using a microservice architecture, where experiments and all required management services are running inside containers. The performance of the RPiaaS testbed is evaluated using several benchmark experiments. The obtained results not only illustrate that the overhead of both using containers and running the required RPiaaS services is minimal but also provide useful insights for scaling up experiments between the Raspberry Pi testbed and a larger more traditional cloud testbed. The introduced validation approach is then illustrated using a case study focusing on the allocation of hierarchically structured tenant data. The results obtained through simulations are compared to the experimental results. The RPiaaS testbed proved to be a very useful tool for the initial experimental validation before moving the experiments to a large‐scale testbed.

Automated management and orchestration of cloud applications have become increasingly important, partly due to the large skills shortage in IT operations and the increasing complexity of cloud applications. Cloud modeling languages play an important role in this, both for describing the structure of a cloud application and specifying the management actions around it. The TOSCA cloud model standard recently defined declarative workflows as the preferred way to specify these management actions but, as noted in the standard itself, this is far from ideal. This paper draws lessons from six years of using declarative workflows in Juju for deploying and managing complex platforms such as OpenStack and Kubernetes in production. This confirms the limitations: declarative workflows are inflexible, hard to reuse, and allow for related components to become silently incompatible. This paper proposes the reactive pattern to solve these issues by enabling the creation of emergent workflows using declarative flags and handlers, which can be easily grouped into reusable layers. After more than two years of using this pattern in production as part of our charms. reactive framework, it is clear that it enables reusability and ensures compatibility: 67% of reactive charms share parts of the management workflow and 73% of reactive charms share a relationship workflow.

Network Functions Virtualization (NFV) presents several advantages over traditional network architectures, such as flexibility, security, and reduced CAPEX/OPEX. However, virtualizing network functions usually executed on specialized hardware (e.g., firewall, DPI, load balancer) and employing innovative technologies (e.g., OpenFlow, P4) increases the challenges of designing, testing, and deploying network infrastructures and services. Although platforms for prototyping NFV environments have emerged in recent years, they still present limitations that hinder the evaluation of specific NFV scenarios, such as fog computing and heterogeneous networks. In this paper, we present NIEP: a platform for designing and testing NFV-based infrastructures and Virtualized Network Functions (VNFs) through the integration of a well-known network emulator (Mininet) and a novel platform for Click-based VNFs development (Click-on- OSv). NIEP provides a complete NFV emulation environment, allowing network operators to test their solutions in a controlled scenario prior to deployment in production networks. As main advantages, NIEP allows the emulation of heterogeneous scenarios, which can be easily migrated to production environments. An experimental scenario is defined to analyze NIEP's performance in terms of VNFs boot time and throughput. Further, NIEP's advantages and shortcomings are discussed and compared to existing emulation platforms.

The advent of big data analytics and cloud computing technologies has resulted in wide-spread research in finding solutions to the data placement problem, which aims at properly placing the data items into distributed datacenters.Although traditional schemes of uniformly partitioning the data into distributed nodes is the defacto standard for many popular distributed data stores like HDFS or Cassandra, these methods may cause network congestion for data-intensive services, thereby affecting the system throughput.This is because as opposed to MapReduce style workloads, data-intensive services require access to multiple datasets within each transaction.In this paper, we propose a scalable method for performing data placement of data-intensive services into geographically distributed clouds.The proposed algorithm partitions a set of data-items into geodistributed clouds using spectral clustering on hypergraphs.Additionally, our spectral clustering algorithm leverages randomized techniques for obtaining low-rank approximations of the hypergraph matrix, thereby facilitating superior scalability for computation of the spectra of the hypergraph laplacian.Experiments on a real-world trace-based online social network dataset show that the proposed algorithm is effective, efficient, and scalable.Empirically, it is comparable or even better (in certain scenarios) in efficacy on the evaluated metrics, while being up to 10 times faster in running time when compared to state-of-the-art techniques.

The demand of Virtual Reality (VR) video streaming to mobile devices is booming, as VR becomes accessible to the general public. However, the variability of conditions of mobile networks affects the perception of this type of high-bandwidth-demanding services in unexpected ways. In this situation, there is a need for novel performance assessment models fit to the new VR applications. In this paper, we present PERCEIVE, a two-stage method for predicting the perceived quality of adaptive VR videos when streamed through mobile networks. By means of machine learning techniques, our approach is able to first predict adaptive VR video playout performance, using network Quality of Service (QoS) indicators as predictors. In a second stage, it employs the predicted VR video playout performance metrics to model and estimate end-user perceived quality. The evaluation of PERCEIVE has been performed considering a real-world environment, in which VR videos are streamed while subjected to LTE/4G network condition. The accuracy of PERCEIVE has been assessed by means of the residual error between predicted and measured values. Our approach predicts the different performance metrics of the VR playout with an average prediction error lower than 3.7% and estimates the perceived quality with a prediction error lower than 4% for over 90% of all the tested cases. Moreover, it allows us to pinpoint the QoS conditions that affect adaptive VR streaming services the most.

Nowadays, news-based websites and portals provide significant amounts of multimedia content to accompany news stories and articles. Within this context, HTTP Adaptive Streaming is generally used to deliver video over the best-effort Internet, allowing smooth video playback and a good Quality of Experience (QoE). To stimulate user engagement with the provided content, such as browsing and switching between videos, reducing the video's startup time has become more and more important: while the current median load time is in the order of seconds, research has shown that user waiting times must remain below two seconds to achieve an acceptable QoE. We developed a framework for low-latent delivery of news-related video content, integrating four optimizations either at server-side, client-side, or at the application layer. Using these optimizations, the video's startup time can be reduced significantly, allowing user interaction and fast switching between available content. In this paper, we describe a proof of concept of this framework, using a large dataset of a major Belgian news provider. A dashboard is provided, which allows the user to interact with available video content and assess the gains of the proposed optimizations. Particularly, we demonstrate how the proposed optimizations consistently reduce the video's startup time in different mobile network scenarios. These reductions allow the news provider to improve the user's QoE, reducing the startup time to values well below two seconds in different mobile network scenarios.

Remote collaboration is common nowadays in conferencing, tele-health and remote teaching applications. To support these interactive use cases, Real-Time Communication (RTC) solutions, as the open-source WebRTC framework, are generally used. WebRTC is peer-to-peer by design, which entails that each sending peer needs to encode a separate, independent stream for each receiving peer in the remote session. This approach is therefore expensive in terms of number of encoders and not able to scale well for a large number of users. To overcome this issue, a WebRTC-compliant framework is proposed in this paper, where only a limited number of encoders are used at sender-side. Consequently, each encoder can transmit to a multitude of receivers at the same time. The conference controller, a centralized Selective Forwarding Unit (SFU), dynamically forwards the most suitable stream to each of the receivers, based on their bandwidth conditions. Moreover, the controller dynamically recomputes the encoding bitrates of the sender, to follow the long-term bandwidth variations of the receivers and increase the delivered video quality. The benefits of this framework are showcased using a demo implemented using the Jitsi-Videobridge software, a WebRTC SFU, for the controller and the Chrome browser for the peers. Particularly, we demonstrate how our framework can improve the received video quality up to 15% compared to an approach where the encoding bitrates are static and do not change over time.

Web portals, such as the one hosted by news providers, have recently started to provide significant amounts of multimedia content. To deliver this content over the best-effort Internet, HTTP Adaptive Streaming (HAS) is generally used, allowing smoother playback and a better Quality of Experience (QoE). To stimulate user engagement with the provided content, reducing the video's startup time has become more and more important: while the current median video load time is in the order of seconds, research has shown that user waiting times must remain below two seconds to achieve an acceptable QoE. In this work, we present a framework for low-latency delivery of news-related video content, integrating four optimizations either at server-side, client-side, or at the application layer. Most importantly, we propose to identify relevant content through user profiling, using proactive delivery and client-side caching to reduce the video startup time. By means of a large data set from a Belgian news provider, we show that the proposed framework can reduce the startup time from 4.6 s to 1.5 s (-74.6%) in a 3G scenario, at the cost of limited network overhead and additional complexity at server- and client-side.

Remote teaching applications are common nowa-days. Very often, these applications resemble video-on-demand streaming platforms rather than real virtual classrooms, where a group of students (the receivers) can remotely attend a live lecture held by a lecturer (the sender). To better support this live scenario, Real-Time Communication (RTC) solutions can be used. WebRTC is an open-source project for real-time browser- based conferencing, developed with a peer-to-peer architecture in mind. To use WebRTC, each receiver requires a dedicated encoder at sender-side. Using such approach is expensive in terms of encoders, and does not scale well for a large number of users. To overcome this issue, a WebRTC-compliant framework is proposed, where only a limited number of encoders are used. A centralized node, the conference controller, dynamically forwards the most suitable stream to the receivers, based on their bandwidth conditions. Moreover, the controller dynamically recomputes the encoding bitrates of the sender. This approach allows to closely follow the long-term bandwidth variations of the receivers, even with a limited number of encoders at sender-side. To evaluate the performance of the proposed framework in a realistic environment, a testbed has been implemented using the Chrome browser and the open-source Jitsi-Videobridge. In a scenario with 10 receivers and 3 encoders, and under realistic network conditions, the proposed framework improves the received video bitrate up to 11%, compared to a static solution where the encoding bitrates do not change over time.

In recent years, the Internet of Things (IoT) has introduced a whole new set of challenges and opportunities in Telecommunications. Traffic over wireless networks has been increasing exponentially since many sensors and everyday devices are being connected. Current networks must therefore adapt to and cope with the specific requirements introduced by IoT. One fundamental need of the next generation networked systems is to monitor IoT applications, especially those dealing with personal health monitoring or emergency response services, which have stringent latency requirements when dealing with malfunctions or unusual events. Traditional anomaly detection approaches are not suitable for delay-sensitive IoT applications since these approaches are significantly impacted by latency. With the advent of 5G networks and by exploiting the advantages of new paradigms, such as Software-Defined Networking (SDN), Network Function Virtualization (NFV) and edge computing, scalable, low-latency anomaly detection becomes feasible. In this paper, an anomaly detection solution for Smart City applications is presented, focusing on low-power Fog Computing solutions and evaluated within the scope of Antwerp's City of Things testbed. Based on a collected large dataset, the most appropriate Low Power Wide Area Network (LPWAN) technologies for our Smart City use case are investigated.

Automatic scaling of complex software-as-a-service application workflows is one of the most important problems concerning resource management in clouds. In this paper, we study the automatic workflow resource scaling problem for streaming and ASAP workflows, and its time-varying variant where the workflow resource requirements change over time. Service components of streaming workflows execute concurrently while those of ASAP workflows execute sequentially. We propose an intelligent framework, BRAHMA+, which possesses the capability to learn the workflow behavior and construct a knowledge base that serves as its decision making engine. The proposed resource provisioning algorithms leverage this learned information curated in the knowledge base to perform informed and intelligent scaling decisions. Additionally, BRAHMA+ employs the use of online-learning strategies to keep the knowledge base up-to-date, thereby accommodating the changes in the workflow resource requirements over time. We evaluate the proposed algorithms using CloudSim simulations. Results on streaming and ASAP workflows, with both static and time-varying resource requirements show that the proposed algorithms are effective and produce good cost-quality trade-offs. The proactive and hybrid algorithms meet the service level agreements and restrict deadline violations to a small fraction (3%-5% in the considered scenarios), while only suffering a marginal increase in average cost per component compared to the described baseline algorithms.

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming (HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users’ QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users’ QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users’ QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server- and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years.

With cloud computing, the efficient management of resources is of great importance as an increased utilization of the available resources can result in higher scalability and significant energy and cost reductions.Experimental validation of novel resource management strategies is costly and time consuming, and often requires in-depth knowledge of and control over the underlying cloud platform.As a result, many novel strategies are only evaluated by means of simulations, in which the whole cloud computing environment is modelled and simulated.Nonetheless, experimental validation should also be considered during the validation, as these types of experiments can often result in new insights or they can be used to fine-tune some specific parameters.In this paper we present a general approach for the experimental validation of cloud resource management strategies, together with the introduction of a cloud testbed adapter which was designed to facilitate the step from simulations towards experimental validation on physical cloud testbeds.We illustrate our solution by means of two case studies, focusing on two different types of testbeds.The adapter mainly acts as a dispatcher towards specific services of the evaluated cloud setup, and allows researchers to easily validate their ideas without having to dive deep into the complex details of the underlying cloud platform.

Big data storage technologies inherently entail high latency characteristics, preventing users from performing efficient ad-hoc querying and interactive visualization on large and distributed datasets. Most of the existing approaches addressing this issue thrive on de-normalization of the static data schema and creation of application specific (i.e. hard-coded) materialized views, which certainly reduce data access latency but at the expense of flexibility. In this regard, this paper proposes an approach that relies on an iterative process of data transformation intended to generate read-optimized data schemas. The transformation process is able to automatically identify optimization opportunities (e.g. materialized views, missing indexes), by analyzing the original data schema and the record of queries issued by users and client applications against the data set. An experimental evaluation of the proposed approach evidences a significant reduction in the query latency, ranging from 81.60% to 99.99%.

In the last years, traffic over wireless networks has been increasing exponentially, due to the impact of Internet of Things (IoT) and Smart Cities. Current networks must adapt to and cope with the specific requirements of IoT applications since resources can be requested on-demand simultaneously by multiple devices on different locations. One of these requirements is low latency, since even a small delay for an IoT application such as health monitoring or emergency service can drastically impact their performance. To deal with this limitation, the Fog computing paradigm has been introduced, placing cloud resources on the edges of the network to decrease the latency. However, deciding which edge cloud location and which physical hardware will be used to allocate a specific resource related to an IoT application is not an easy task. Therefore, in this paper, an Integer Linear Programming (ILP) formulation for the IoT application service placement problem is proposed, which considers multiple optimization objectives such as low latency and energy efficiency. Solutions for the resource provisioning of IoT applications within the scope of Antwerp's City of Things testbed have been obtained. The result of this work can serve as a benchmark in future research related to placement issues of IoT application services in Fog Computing environments since the model approach is generic and applies to a wide range of IoT use cases.

In media production companies, exchanging large media files is daily business. Due to the predictable nature of network transfers in the media production industry, timeslot-based advance bandwidth reservation results in higher bandwidth utilization and improved network performance. Timeslot-based advance reservation can be based on flexible or fixed timeslot sizes. As the flexible approach is highly beneficial under bursty and limited network traffic conditions, this paper focuses on that approach. We design, implement and evaluate a novel algorithm based on flexible timeslots, taking into account the specific characteristics of media transfers and compare it with a fixed timeslot algorithm to quantitatively study the quality and complexity of both scenarios. We have defined a set of realistic media production use cases that serve as a basis for the evaluations. Results shows that the highest admittance ratio is consistently achieved by using the flexible time interval algorithm, while the execution time of this approach is up to 12 times lower, compared to the approach with fixed timeslot sizes.

With the increasing popularity of multimedia-rich web portals, reducing latency has become more and more important. The current median web page load time is in the order of seconds, while research has shown that user waiting times must remain below two seconds to achieve optimal acceptance. In this paper, we analyzed a large dataset obtained from a major Belgian news provider, focusing on content popularity, user activity and user preference towards article news categories. Based on this analysis, we introduce the concept of personal delivery networks (PDNs), in which content is stored closer to the end user, at delivery caches in the edge of the core network or even in the access network. PDN nodes proactively prefetch and evict content on a per-user basis, opening opportunities for personalized low-latency delivery of multimedia-rich web applications. Initial results show that a PDN-based approach allows to significantly reduce the average latency.

The increased popularity of social media and mobile devices has radically changed the way people consume multimedia content online. As an example, users can experience the same event (e.g. a sports event or a concert) together using social media, even if they are not in the same physical location. Moreover, the introduction of the HTTP Adaptive Streaming principle has made it possible to deliver video over the best-effort Internet with consistent quality, even for mobile devices. One of the challenges within this context is the synchronization of multimedia playback among geographically distributed clients. To solve this issue, we propose a Web-based framework which allows to synchronize the playback of different clients. We also present a novel hybrid approach for adaptive streaming to allow fast synchronization among different clients, which relies on HTTP/2's server push feature in combination with sub-second video segments. In this paper, we detail the proposed framework and provide a comprehensive analysis of its performance. Experiments show that the novel hybrid approach can reduce synchronization time with 19.4% compared to standard adaptive streaming over HTTP/1.1 when bandwidth is limited to 2.5 Mb/s and an RTT of 150 ms. The gain increases even more when a higher throughput is available. The obtained results entail that the proposed framework can provide quality of experience for all users watching online video together.

Virtual Reality (VR) devices are becoming accessible to a large public, which is going to increase the demand for 360° VR videos. VR videos are often characterized by a poor quality of experience, due to the high bandwidth required to stream the 360° video. To overcome this issue, we spatially divide the VR video into tiles, so that each temporal segment is composed of several spatial tiles. Only the tiles belonging to the viewport, the region of the video watched by the user, are streamed at the highest quality. The other tiles are instead streamed at a lower quality. We also propose an algorithm to predict the future viewport position and minimize quality transitions during viewport changes. The video is delivered using the server push feature of the HTTP/2 protocol. Instead of retrieving each tile individually, the client issues a single push request to the server, so that all the required tiles are automatically pushed back to back. This approach allows to increase the achieved throughput, especially in mobile, high RTT networks. In this paper, we detail the proposed framework and present a prototype developed to test its performance using real-world 4G bandwidth traces. Particularly, our approach can save bandwidth up to 35% without severely impacting the quality viewed by the user, when compared to a traditional non-tiled VR streaming solution. Moreover, in high RTT conditions, our HTTP/2 approach can reach 3 times the throughput of tiled streaming over HTTP/1.1, and consistently reduce freeze time. These results represent a major improvement for the efficient delivery of 360° VR videos over the Internet.

The demand for 360° Virtual Reality (VR) videos is expected to grow in the near future, thanks to the diffusion of VR headsets. VR Streaming is however challenged by the high bandwidth requirements of 360° videos. To save bandwidth, we spatially tile the video using the H.265 standard and stream only tiles in view at the highest quality. The video is also temporally segmented, so that each temporal segment is composed of several spatial tiles. In order to minimize quality transitions when the user moves, an algorithm is developed to predict where the user is likely going to watch in the near future. Consequently, predicted tiles are also streamed at the highest quality. Finally, the server push in HTTP/2 is used to deliver the tiled video. Only one request is sent from the client; all the tiles of a segment are automatically pushed from the server. This approach results in a better bandwidth utilization and video quality compared to traditional streaming over HTTP/1.1, where each tile has to be requested independently by the client. We showcase the benefits of our framework using a prototype developed on a Samsung Galaxy S7 and a Gear VR, which supports both tiled and non-tiled videos and streaming over HTTP/1.1 and HTTP/2. Under limited bandwidth conditions, we demonstrate how our framework can improve the quality watched by the user compared to a non-tiled solution where all of the video is streamed at the same quality. This result represents a major improvement for the efficient streaming of VR videos.

With the increasing deployments of Network Functions Virtualization (NFV) in both industry and academia, it becomes necessary to design mechanisms for keeping the integrity of Service Function Chains (SFC) responsible for NFV services delivering. Despite the advances in the development of management and orchestration for NFV, solutions to keep SFCs resilient to well-known and zero-day threats are still much needed. In this paper, we introduce a framework for deploying anomaly detection techniques for SFC in NFV environments. Our framework consists of a set of functional blocks with well-defined functions, composing an additional SFC Integrity Module (SIM) for the standard NFV architecture. The proposed SIM enables NFV orchestrators to analyze NFV elements and perform suggested actions with the goal of keeping service integrity in the network. The results obtained through the evaluation of a Proof-of-Concept implementation show that the proposed framework is able to properly detect different types of anomalies using entropy-based detection techniques.

Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov decision processes (MDPs). It is generally thought that count-based methods cannot be applied in high-dimensional state spaces, since most states will only occur once. Recent deep RL exploration strategies are able to deal with high-dimensional continuous state spaces through complex heuristics, often relying on optimism in the face of uncertainty or intrinsic motivation. In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks. States are mapped to hash codes, which allows to count their occurrences with a hash table. These counts are then used to compute a reward bonus according to the classic count-based exploration theory. We find that simple hash functions can achieve surprisingly good results on many challenging tasks. Furthermore, we show that a domain-dependent learned hash code may further improve these results. Detailed analysis reveals important aspects of a good hash function: 1) having appropriate granularity and 2) encoding information relevant to solving the MDP. This exploration strategy achieves near state-of-the-art performance on both continuous control tasks and Atari 2600 games, hence providing a simple yet powerful baseline for solving MDPs that require considerable exploration.

Unmanned Aerial Vehicle (UAV) technology is advancing at a fast pace following the strong rise in interest in its applications for a wide variety of scenarios. One of the promising use cases for UAVs is their deployment during emergency and rescue operations. Their high mobility, aerial viewpoint and flexibility to be operated autonomously are huge assets during crises. UAVs exist in a wide range when it comes to cost, and so do the sensors or accessories they can carry along as payload. During emergencies it may be beneficial to have several low-cost UAVs on site, as opposed to one highly sophisticated, fully-equipped aerial platform. This way more ground can be covered in a shorter span of time during search-and-rescue operations. The situation where a single UAV is on the ground to recharge its battery can also be avoided. In this paper, we present an architecture that identifies and locates objects of interest in real-time using low-cost hardware and a state of the art object detection algorithm. We avoid the use of expensive LIDAR sensors and UAVs, but still manage to determine the position of specific predefined objects in the field with high precision. Without augmenting our detection setup by using intelligent adaptive flight patterns, we can locate an object within 1.5m of its actual location at very low latency. Our detection chain delivers the result and its location in well under one second.

Network function virtualization (NFV) and software defined networking (SDN) have been proposed to increase the cost-efficiency, flexibility, and innovation in network service provisioning. This is achieved by leveraging IT virtualization techniques and combining them with programmable networks. By doing so, NFV and SDN are able to decouple the network functionality from the physical devices on which they are deployed. Service function chains (SFCs) composed out of virtual network functions (VNFs) can now be deployed on top of the virtualized infrastructure to create new value-added services. Current NFV approaches are limited to mapping the different VNF to the physical substrate subject to resource capacity constraints. They do not provide the possibility to define location requirements with a certain granularity and constraints on the colocation of VNF and virtual edges. Nevertheless, many scenarios can be envisioned in which a service provider (SP) would like to attach placement constraints for efficiency, resilience, legislative, privacy, and economic reasons. Therefore, we propose a set of affinity and anti-affinity constraints, which can be used by SP to define such placement restrictions. Furthermore, a semantic SFC validation framework is proposed that allows the virtual network function infrastructure provider (VNFInP) to check the validity of a set of constraints and provide feedback to the SPs. This allows the VNFInP to filter out any non-valid SFC requests before sending them to the mapping algorithm, significantly reducing the mapping time.

Softwarization of networks is an important trend, enabled by the NV (Network Virtualization), SDN (Software-Defined Networking), and NFV (Network Function Virtualization) paradigms and offers many advantages for network operators, service providers and datacenter providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special issues was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on management of softwarized networks.

Traditional infrastructure models are being replaced by Cloud-enabled models with support for several different applications/services. One of these services is professional real-time Audio/Video collaboration. Although high-end business collaboration solutions are nowadays offered with high reliability, they rely on dedicated specialized (and costly) hardware and network infrastructure setups, and usually prove difficult to scale up. On the other hand, existing cloud-based A/V collaboration solutions do allow for easier elasticity and scalability, but do so on a best-effort basis with no guarantees for reliability or quality. In this context, this paper proposes cloud-enabled A/V collaboration resource allocation algorithms focused on solving the business requirements and SLAs dealing with elasticity and scalability. The proposed algorithms attempt to minimise Cloud resource usage, in order to keep the cost within budget, while striving for the reliability and quality of dedicated hardware solutions. The evaluation is conducted using a version of the CloudSim simulator that has been extended to support collaborative meeting patterns, usage seasonality, resource expenditure prediction, network congestion and streaming multicast. Compared to the second best solution applied to the same evaluated scenario, our results show a reduction of bandwidth usage and virtual machine costs by 49% and 63%, respectively, and an effectiveness of 99% in meeting A/V collaboration setup deadlines.

A key challenge in the realization of autonomous vehicles is the machine's ability to perceive its surrounding environment. This task is tackled through a model that partitions vehicle camera input into distinct semantic classes, by taking into account visual contextual cues. The use of structured machine learning models is investigated, which not only allow for complex input, but also arbitrarily structured output. Towards this goal, an outdoor road scene dataset is constructed with accompanying fine-grained image labelings. For coherent segmentation, a structured predictor is modeled to encode label distributions conditioned on the input images. After optimizing this model through max-margin learning, based on an ontological loss function, efficient classification is realized via graph cuts inference using alpha-expansion. Both quantitative and qualitative analyses demonstrate that by taking into account contextual relations between pixel segmentation regions within a second-degree neighborhood, spurious label assignments are filtered out, leading to highly accurate semantic segmentations for outdoor scenes.

The data science skills shortage means that those who have the knowledge are under constant pressure to do more with less. While the data science tools are improving at a staggering pace, the operational tools around them can not keep up. Even researchers at Google state that the issue of automatic configuration and dependency management of services is still an “open, hard problem”. This manifests itself in data scientists either constantly having to solve operational challenges or having to be in constant close collaboration with a skilled operations team. This paper addresses the operational challenges behind deploying and managing workflows on top of analytics platforms by starting from three key requirements: data scientists want to model their workflows in a reusable way, this model should be automatically deployed, managed and connected to other services, and this solution should be compatible with existing cloud modeling languages, infrastructure, analytics platforms and tools. The paper explores where the state-of-the-art falls short in meeting these requirements, proposes an architecture to solve the open challenges, and implements and evaluates this architecture.

Cloud-based Software-as-a-Service (SaaS) providers want to grow into the space of business process outsourcing (BPO). BPO refers to the systematic and controlled delegation of many steps of a company's business process. BPO is a new and important extension to SaaS, as it allows the provider to add more value in the online application services and as it enables the outsourcer to obtain more cost efficiency. BPO results in decentralized federated workflows. To describe these workflows, companies often use business process modeling languages. Currently, Business Process Modeling Notation (BPMN) is one of the best-known standards. It is crucial to ascertain that the modeled workflow is executed as intended. Errors that happen during execution of a federated workflow can come with huge costs. Validating the model is limited to syntactical checks and there is little support for validating the execution at design time. In this paper a method is presented to validate the correct execution of BPMN 2.0 Collaborations. The methods in this research use concepts from virtual time previously described for Web Services Choreography Description Language (WS-CDL). To validate the results of this research, the Eclipse BPMN modeler was extended with an implementation of the validation method.

Current Cloud software development efforts to come up with novel Software-as-a-Service (SaaS) applications are, just like traditional software development, usually no longer built from scratch. Instead more and more Cloud developers are opting to use multiple existing components and integrate them in their application workflow. Scaling the resulting application up or down, depending on user/tenant load, in order to keep the SLA, no longer becomes an issue of scaling resources for a single service, rather results in a complex problem of scaling all individual service endpoints in the workflow, depending on their monitored runtime behavior. In this paper, we propose and evaluate algorithms through CloudSim for automatic and runtime scaling of such multi-tenant SaaS workflows. Our results on time-varying workloads show that the proposed algorithms are effective and produce the best cost-quality trade-off while keeping Service Level Agreements (SLAs) in line. Empirically, the proactive algorithm with careful parameter tuning always meets the SLAs while only suffering a marginal increase in average cost per service component of approximate to 5-8% over our baseline passive algorithm, which, although provides the least cost, suffers from prolonged violation of service component SLAs.

Real-time Audio/Video (A/V) collaboration is an application domain for which Cloud adoption is being investigated. However, the professional market generally prefers reliability over elasticity / scalability, in terms of managed delay and quality, and hence usually relies on dedicated specialized hardware and network setups, which are costly and hard to scale. This paper proposes cloud-enabled A/V resource allocation algorithms that combine elasticity and scalability with a focus on solving professional SLA requirements. The proposed algorithms attempt to minimize cloud cost and network usage by intelligently provisioning service endpoints along distributed data centers and splitting A/V streaming content in selected endpoints, while striving for the measurable and controlled reliability offered by dedicated solutions. An extended version of the CloudSim simulator, developed to generate and simulate A/V collaboration patterns while collecting statistics about resource usage and cost, network congestion and delay, among others, was used for evaluation purposes. When comparing results between the most efficient proposed approach and the second one, bandwidth usage and virtual machine costs are reduced by 60% and 65%, respectively, while maintaining industry-accepted SLA compliance levels.

The prevalent use of multi-component, multi-tenant models for building novel Software-as-a-Service (SaaS) applications has resulted in wide-spread research on automatic scaling of the resultant complex application workflows. In this paper, we propose a holistic solution to Automatic Workflow Scaling under the combined presence of Streaming and Deadline-critical workflows, called AWS-SD. To solve the AWS-SD problem, we propose a framework BRAHMA, that learns workflow behavior to build a knowledge-base and leverages this info to perform intelligent automated scaling decisions. We propose and evaluate different resource provisioning algorithms through CloudSim. Our results on time-varying workloads show that the proposed algorithms are effective and produce good cost-quality trade-offs while preventing deadline violations. Empirically, the proposed hybrid algorithm - combining learning and monitoring, is able to restrict deadline violations to a small fraction (3-5%), while only suffering a marginal increase in average cost per component of 1-2% over our baseline naïve algorithm, which provides the least costly provisioning but suffers from a large number (35-45%) of deadline violations.

HTTP Adaptive Streaming (HAS) has become the de facto standard technology for the delivery of video streaming services. Current adaptation heuristics for HAS focus on the selection of the optimal quality representation to be delivered from a single server. However, many content providers use multiple content servers storing replicas of the segmented video or are deployed over Content Delivery Networks (CDNs). Hence, the problem is not limited to selecting the optimal quality but also consists in requesting the segments from the best performing video server. In this paper a dynamic server selection strategy is proposed that enables the streaming client to select the optimal video delivery server. The proposed mechanism allows any quality adaptation algorithm to be plugged into it. The selection algorithm uses probability-based search strategies to explore the search space of available servers and to gain insights in their characteristics. This prevents the selection strategy to end up in a local optimum. To avoid buffer starvations, the exploration behavior is dependent on the current buffer filling. The proposed approach allows to achieve a Quality of Experience (QoE) that is within 25% of the optimum for which the client has a priori knowledge of the server characteristics.

Video streaming services have continuously gained popularity over the last decades, accounting for about 70% of all consumer Internet traffic in 2016. All of these video streaming sessions have strict delivery deadlines in order to avoid playout interruptions, detrimentally impacting the Quality of Experience (QoE). However, the vast majority of this traffic uses TCP at the transport layer, which is known to be far from minimizing the number of deadline-missing streams. By introducing deadline-awareness at the transport layer, video delivery can be optimized by prioritizing specific flows. This paper proposes a deadline-aware congestion control mechanism, based on a parametrization of the traditional TCP New Reno congestion control strategy. By taking into account the available deadline information, the modulation of the congestion window is dynamically adapted to steer the aggressiveness of a considered stream. The proposed approach has been thoroughly evaluated in both a video-on-demand (VoD)-only scenario and a scenario where VoD streams co-exist with live streaming sessions and non-deadline-aware traffic. It was shown that in a video streaming scenario the minimal bottleneck bandwidth can be reduced by 16% on average when using deadline-aware congestion control. In coexistence with other TCP traffic, a bottleneck reduction of 11% could be achieved.

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services over the Internet. In HAS, each video is segmented and stored in different qualities. Rate adaptation heuristics, deployed at the client, allow the most appropriate quality level to be dynamically requested based on the current network conditions, in order to achieve a continuous playout. Due to the ability of HAS protocols to dynamically adapt to bandwidth fluctuations, they are especially suited for the delivery of multimedia content in mobile environments. However, current HAS solutions do not take the battery lifetime into account, which is a typical issue for mobile devices. In this paper, we therefore propose an energy-aware heuristic for HAS. We first present a measurement study to identify and quantify the main factors influencing the battery lifetime on mobile devices. We then develop a heuristic based on these findings, which optimizes both the quality of experience and the battery consumption of a video streaming session. Particularly, we found that the video resolution and display size have the highest impact on the battery lifetime and that our energy-aware heuristic can prolong a streaming session with up to 13%, compared to a standard HAS heuristic. This result represents a consistent improvement for the overall user experience on battery-constrained devices.

In education, learners no longer perform learning activities in a well-defined and static environment like a physical classroom. Digital learning environments promote learners anytime, anywhere and anyhow learning. As such, the context in which learners undertake these learning activities can be very diverse. To optimize learning and the environment in which it occurs, learning analytics measure data about learners and their context. Unfortunately, current state of the art standards and systems are limited in capturing the context of the learner. In this paper we present a Learning Record Store (LRS), compatible with the Experience API, that is able to capture the learners' context, more concretely his location and used device. We use ontologies to model the xAPI and context information. The data is stored in a RDF triple store to give access to different services. The services will show the advantages of capturing context information. We tested our system by sending statements from 100 learners completing 20 questions to the LRS.

HTTP Adaptive Streaming (HAS) is the de facto standard for video streaming services over the Internet. In HAS, each video is temporally segmented and stored in different qualities. The client selects the quality level for every video segment based on network conditions, allowing a smooth playback with the best possible Quality of Experience (QoE). Although results are promising, current solutions suffer from two problems. First, a low quality and large end-to-end latency are often observed in live streaming scenarios. Second, freezes in the video playout may occur in case of sudden drops of the available bandwidth. We reduced these issues using two complementary approaches. First, we reduced the live latency using the new HTTP/2 server push in combination with super-short segments. Second, we designed an OpenFlow-based network controller that prioritizes the delivery of particular segments to avoid freezes at the clients. The proof-of-concept shows the results obtained when two clients stream a video under varying network conditions. By monitoring the clients' behavior, it is possible to understand the gains brought by the proposed approaches. Particularly, we demonstrate how our solutions consistently reduce the live latency in high round-trip time networks and video freezes caused by network congestion. These results represent a major improvement for the QoE of the final users.

Scalable and effective exploration remains a key challenge in reinforcement learning (RL). While there are methods with optimality guarantees in the setting of discrete state and action spaces, these methods cannot be applied in high-dimensional deep RL scenarios. As such, most contemporary RL relies on simple heuristics such as epsilon-greedy exploration or adding Gaussian noise to the controls. This paper introduces Variational Information Maximizing Exploration (VIME), an exploration strategy based on maximization of information gain about the agent's belief of environment dynamics. We propose a practical implementation, using variational inference in Bayesian neural networks which efficiently handles continuous state and action spaces. VIME modifies the MDP reward function, and can be applied with several different underlying RL algorithms. We demonstrate that VIME achieves significantly better performance compared to heuristic exploration methods across a variety of continuous control tasks and algorithms, including tasks with very sparse rewards.

Due to the predictable nature of network transfers in media production industry, advance bandwidth reservation results in higher bandwidth utilization and improved network performance. However, in unreliable networks, this may fail. As a first provisional stage, deploying protection mechanisms ensures that the schedule remains valid when the system is in operation. Constant monitoring and modification is also required in order to be capable of dynamically adapting the network to changing conditions. In this paper, we propose an efficient dual approach consisting of two processes. First, a schedule is produced by a resilient advance reservation algorithm. Then, the generated schedule is continually updated over time using a runtime adaptation approach. As this step uses the interconnecting network links' leftover capacity, following this approach leads to increased performance in case of steady network conditions, or neutral performance when transmitting admitted requests in uncertain network conditions.

HTTP Adaptive Streaming (HAS) is the de facto standard for over-the-top video streaming. In HAS, video content is encoded at multiple quality levels and temporally divided into multiple segments. The client can select the quality level for every video segment, allowing smoother playback and a better Quality of Experience (QoE). Although results are promising, current solutions often suffer from high round-trip time (RTT) cycles in mobile networks. This is especially true for scalable video coding (SVC), where multiple requests are required to retrieve a single video segment. Meanwhile, the IETF has standardized the HTTP/2 protocol since February 2015, providing new features that allow a reduction of the page load time in Web browsing. In this paper, we propose a novel approach based on HTTP/2's server push feature to actively push the base layer of live, SVC-encoded content from server to client. This allows to eliminate one RTT cycle for every video segment, which has a significant impact on the user's QoE. Evaluating the proposed approach, we show that compared with HTTP/1.1, an improvement of 65.42% can be achieved for the average video quality in high-RTT networks. Compared to an AVC-based solution, the freeze frequency and duration are reduced by 54.55% and 53.06% respectively, while the loss in video quality is limited to 4.51%. Since playout freezes should be avoided at the cost of a lower video quality, we conclude that the proposed approach beneficially impacts the user's QoE.

Cloud computing is closely related to multi-tenancy, as it relies on resources that are shared among multiple clients. The provisioning and management of storage resources for cloud applications is an interesting research topic, as reallocation of data over time should be minimised, and the developed strategy should guarantee both data separation and performance isolation for every tenant. In this demo, we present a simulation tool for evaluating and comparing different data allocation strategies. Evaluation using real implementations can be very expensive and time consuming, and is not always possible, due to the scale and complexity of the infrastructure on which they are intended to run. The simulator aids as a tool for inexpensive and rapid evaluation of new techniques, and to validate and finetune new data allocation strategies.

In recent years, there has been a rising interest in cloud computing, which is often used to offer Software-as-a-Service (SaaS) over the Internet. SaaS can be offered to clients at a lower cost as it is usually multi-tenant: many end users make use of a single application instance, even when they are from different organizations, reducing resource consumption. This however makes it increasingly hard to offer customization and tailoring options for clients, resulting in limited SaaS customizability. Some applications require high customizability, making it impossible to cost-efficiently offer them using cloud computing. In this thesis we design, develop and evaluate multiple approaches for the development and management of highly customizable multi-tenant SaaS, focusing on an approach that decomposes applications into components and on the modeling of the relations between application components, and present a methodology for managing these applications in cloud datacenters and (traditional and Network Function Virtualization-aware) network environments. Extensive evaluations using simulations based on existing commercial applications and generated scenarios show that the proposed approach can be used to increase multi-tenancy and save costs in the management of highly customizable SaaS.

Cloud computing is growing in adoption for different services previously supported by traditional infrastructure, including dedicated hardware setups. One of these cloud-enabled services is real-time Audio/Video collaboration. Existing cloud-based collaboration systems generally function on a best-effort basis, and offer little to no delivery and quality guarantees. High-quality business-focused solutions are therefore offered using dedicated and often manually configured hardware. In this paper, we present resource provisioning algorithms to provide the mentioned elasticity under strict quality requirements. These algorithms are evaluated, using an extended version of the CloudSim simulator, making use of realistic collaborative meeting patterns prepared to deal with seasonality and usage prediction. Our results show that the algorithms improve costs by up to 98.38% when compared with previously designed more naive approaches and with an effectiveness of 99.9% in meeting A/V collaboration setup deadlines.

In the media production process a substrate network can be shared by many users simultaneously when different media actors are geographically distributed. This allows sophisticated media productions involving numerous producers to be concurrently created and transferred. Due to the predictable nature of media transfers, the collaboration among different actors could be significantly improved by deploying an efficient advance reservation system. In this paper, we propose a model for the advance bandwidth reservation problem, which takes the specific characteristics of media production networks into account. Flexible and time variable bandwidth reservations, meeting delivery deadlines, supporting splittable flows and interdependent transfers and all types of advance reservation requests imposed by the media production transfers are incorporated into this model. In addition to the optimal scheduling algorithms , which are presented based on this model, near optimal alternatives are also proposed. The experimental results show that the proposed algorithms are scalable in terms of physical topology and granularity of time intervals and obtain a satisfactory performance, executing significantly faster than an optimal algorithm and within 8.78% of the optimal results.

Content delivery networks (CDNs) have been the prevalent method for the efficient delivery of content across the Internet. Management operations performed by CDNs are usually applied only based on limited information about Internet Service Provider (ISP) networks, which can have a negative impact on the utilization of ISP resources. To overcome these issues, previous research efforts have been investigating ISP-operated content delivery services, by which an ISP can deploy its own in-network caching infrastructure and implement its own cache management strategies. In this paper, we extend our previous work on ISP-operated content distribution and develop a novel scalable and efficient distributed approach to control the placement of content in the available caching points. The proposed approach relies on parallelizing the decision-making process and the use of network partitioning to cluster the distributed decision-making points, which enables fast reconfiguration and limits the volume of information required to take reconfiguration decisions. We evaluate the performance of our approach based on a wide range of parameters. The results demonstrate that the proposed solution can outperform previous approaches in terms of management overhead and complexity while offering similar network and caching performance.

Summary Cloud computing is a technology that enables elastic, on‐demand resource provisioning, allowing application developers to build highly scalable systems. Multi‐tenancy, the hosting of multiple customers by a single application instance, leads to improved efficiency, improved scalability, and less costs. While these technologies make it possible to create many new applications, legacy applications can also benefit from the added flexibility and cost savings of cloud computing and multi‐tenancy. In this article, we describe the steps required to migrate existing applications to a public cloud environment, and the steps required to add multi‐tenancy to these applications. We present a generic approach and verify this approach by means of two case studies, a commercial medical communications software package mainly used within hospitals for nurse call systems and a schedule planner for managing medical appointments. Both case studies are subject to stringent security and performance constraints, which need to be taken into account during the migration. In our evaluation, we estimate the required investment costs and compare them to the long‐term benefits of the migration. Copyright © 2015 John Wiley & Sons Ltd.

Recently, video-on-demand (VoD) streaming services like Netflix and Hulu have gained a lot of popularity. This has led to a strong increase in bandwidth capacity requirements in the network. To reduce this network load, the design of appropriate caching strategies is of utmost importance. Based on the fact that, typically, a video stream is temporally segmented into smaller chunks that can be accessed and decoded independently, cache replacement strategies have been developed that take advantage of this temporal structure in the video. In this paper, two caching strategies are proposed that additionally take advantage of the phenomenon of binge watching, where users stream multiple consecutive episodes of the same series, reported by recent user behavior studies to become the everyday behavior. Taking into account this information allows us to predict future segment requests, even before the video playout has started. Two strategies are proposed, both with a different level of coordination between the caches in the network. Using a VoD request trace based on binge watching user characteristics, the presented algorithms have been thoroughly evaluated in multiple network topologies with different characteristics, showing their general applicability. It was shown that in a realistic scenario, the proposed election-based caching strategy can outperform the state-of-the-art by 20% in terms of cache hit ratio while using 4% less network bandwidth.

Using Network Functions Virtualization (NFV), network functions are virtualized and chained together to provide a network service. Often, a service chain can be allocated in multiple different ways, making use of different physical or virtual network functions, resulting in varying quality of service and deployment costs. In this paper, we introduce customizable function chains, which model the services within a service chain and their variability, and present a management framework that can be used to deploy these services, making it possible to take service chain variability into account at runtime. We present and evaluate a model for resource allocation of network functions within NFV environments, focusing on a hybrid scenario where part of the network functions may be provided by dedicated physical hardware, and where part of the functions are provided using virtualized instances. We numerically study our approach using a small network provider scenario, on which a connectivity service is deployed, and analyze the benefit of the approach. In our evaluations, we find this approach results in a lower cost compared to an approach which does not support variability, both due to a reduction in the server use cost, and a reduction in failure cost when insufficient resources are present.

There is currently a strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures. This evolution is enabled by three paradigms. First, Software-Defined Networking (SDN) allows network control to be separated from the forwarding plane and allows for a flexible management of the network resources. Second, Network Virtualization (NV) brings virtualization concepts to the network, similar to cloud computing, which was enabled by virtualization of servers. Third, Network Function Virtualization (NFV) focuses on virtualization of software-based network functions. Instead of installing and managing dedicated hardware devices for these functions, they are implemented as software components and deployed on commodity hardware infrastructures, in most cases operated by a network operator or cloud infrastructure provider. Service Function Chaining (SFC) consists of building services using virtual network functions (VNFs). These three paradigms are synergetic and reinforce each other when used together. Several initial SDN, NV and NFV deployments are already operational in providers’ networks.

HTTP Adaptive Streaming (HAS) is quickly becoming the de facto standard for video streaming services. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate level to be dynamically requested, based on the current network conditions. It has been shown that today’s heuristics underperform when multiple clients consume video at the same time, due to fairness issues among clients. Concretely, this means that different clients negatively influence each other as they compete for shared network resources. In this article, we propose a novel rate adaptation algorithm called FINEAS (Fair In-Network Enhanced Adaptive Streaming), capable of increasing clients’ Quality of Experience (QoE) and achieving fairness in a multiclient setting. A key element of this approach is an in-network system of coordination proxies in charge of facilitating fair resource sharing among clients. The strength of this approach is threefold. First, fairness is achieved without explicit communication among clients and thus no significant overhead is introduced into the network. Second, the system of coordination proxies is transparent to the clients, that is, the clients do not need to be aware of its presence. Third, the HAS principle is maintained, as the in-network components only provide the clients with new information and suggestions, while the rate adaptation decision remains the sole responsibility of the clients themselves. We evaluate this novel approach through simulations, under highly variable bandwidth conditions and in several multiclient scenarios. We show how the proposed approach can improve fairness up to 80% compared to state-of-the-art HAS heuristics in a scenario with three networks, each containing 30 clients streaming video at the same time.

Software-as-a-service (SaaS) providers are further expanding their offering by growing into the space of business process outsourcing (BPO). Therefore, the SaaS provider wants to administer and manage the business process steps according to a service level agreement. Outsourcing of business processes results in decentralized business workflows. However, current business process modeling languages, e.g. Business Process Execution Language (BPEL), Business Process Model and Notation (BPMN), are based highly on a centralized execution model and current BPMN engines offer limited constructs for federation and decentralized execution. To guarantee execution of business processes according to a service level agreement, different parties involved in a federated workflow must be able to inspect the state of external workflows. This requires advanced inspection interfaces and monitoring facilities. Current business process modeling languages must thus be extended to support monitoring in the s pecification, support modeling and support deployment of decentralized workflows. In this paper, correlation and monitoring extensions for BPMN are described. These extensions to BPMN are described such that the existing specification can still be used as is in a backwards compatible way.

The growing popularity of over the top (OTT) video streaming services has led to a strong increase in bandwidth capacity requirements in the network. By deploying intermediary caches, closer to the end-users, popular content can be served faster and without increasing backbone traffic. Designing an appropriate replacement strategy for such caching networks is of utmost importance to achieve high caching efficiency and reduce the network load. Typically, a video stream is temporally segmented into smaller chunks that can be accessed and decoded independently. This temporal segmentation leads to a strong relationship between consecutive segments of the same video. Therefore, caching strategies have been developed, taking into account the temporal structure of the video. In this paper, we propose a novel caching strategy that takes advantage of clients announcing which videos will be watched in the near future, e.g., based on predicted requests for subsequent episodes of the same TV show. Based on a Video-on-Demand (VoD) production request trace, the presented algorithm is evaluated for a wide range of user behavior and request announcement models. In a realistic scenario, a performance increase of 11% can be achieved in terms of hit ratio, compared to the state-of-the-art.

Software-Defined Storage (SDS) is an evolving concept in which the management and provisioning of data storage is decoupled from the physical storage hardware. Data-intensive multi-tenant SaaS applications running on the public cloud could benefit from the concepts introduced by SDS by managing the allocation of tenant data from the tenant's perspective, taking custom tenant policies and preferences into account. In this paper, we propose the design of a scalable multi-tenant SDS system. In our approach, tenants are hierarchically clustered based on multiple scenario-specific characteristics. The storage elasticity component of the SDS system is responsible for the dynamic (re-)allocation of tenant data over the available storage resources. It invokes the Hierarchical Bin Packing algorithm introduced in this paper to determine an optimized distribution of tenant data based on the hierarchical tenant tree. We evaluate our system by means of two case studies based on real-life data sets. Experiments confirm that the Hierarchical Bin Packing algorithm achieves a good performance, with execution times below 100 ms to calculate the allocation for 1000 tenants in a worst-case scenario. Furthermore, our system achieves an average utilization of the storage resources close to the configured allocation factor, with reallocation of tenant data balanced over time.

With the maturity of Cloud computing comes research into converting a range of traditionally best effort programs into cloud-enabled services. One such service currently under investigation in the Elastic Media Distribution (EMD) project, is how to enable qualitative, reliable and scalable realtime media collaboration services using proven Cloud technology. While existing best-effort solutions provide plenty of features, they do not provide the quality guarantees and reliability required for critical services in globally distributed corporations. On the other hand, some pricey dedicated solutions do offer these low-delay, reliable cooperation services, but without the benefits that clouds can bring in terms of scalability. In this paper we describe results attained in the EMD project on novel resource provisioning algorithms for a mixture of end-to-end Audio/Video streams with file-based transfers, allowing for configurable tradeoffs between service response time and cost. We extended the CloudSim simulator with models allowing us to simulate collaborative interactive sessions (more specifically educational real-time collaboration), and evaluated the performance of our proposed provisioning heuristics. The results show that the proposed dynamic algorithm allows for automated cost-performance tradeoff by reducing average total Virtual Machine (VM) cost by a maximum of 58% compared to more naive approaches, while keeping average time for clients to join a meeting in line.

Real-Time Travel Information (RTTI) for rail commuters is still used inefficiently today and is rarely combined with other knowledge to come to a truly personalised and situation-aware multimodal travelling assistance. It is up to the travellers themselves to look for important info about their trip through static schedules or dedicated non-personalised applications. In a highly dynamic context such as that of public transportation, it would make life easier if one was able to consult the right information at the right time (removing superfluous information), for a variety of multimodal public transportation options, taking into account the context of the person travelling. In this paper we present the LimeDS framework, allowing application developers to rapidly define data workflows from a variety of data sources, deploy these workflows in a scalable and resilient manner and expose results to client applications as REST endpoints. A Proof-of-Concept (PoC) shows how our proposed framework can be used to tie together different open transportation data sources in order to create highly dynamic multimodal travel assistance applications by semantically enriching the data into knowledge, checking for ontological consistency and reason over the resulting knowledge.

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results.

The popularity of multimedia services offered over the Internet have increased tremendously during the last decade. The technologies that are used to deliver these services are evolving at a rapidly increasing pace. However, new technologies often demand updating the dedicated hardware (e.g., transcoders) that is required to deliver the services. Currently, these updates require installing the physical building blocks at different locations across the network. These manual interventions are time-consuming and extend the Time to Market of new and improved services, reducing their monetary benefits. To alleviate the aforementioned issues, Network Function Virtualization (NFV) was introduced by decoupling the network functions from the physical hardware and by leveraging IT virtualization technology to allow running Virtual Network Functions (VNFs) on commodity hardware at datacenters across the network. In this paper, we investigate how existing service chains can be mapped onto NFV-based Service Function Chains (SFCs). Furthermore, the different alternative SFCs are explored and their impact on network and datacenter resources (e.g., bandwidth, storage) are quantified. We propose to use these findings to cost-optimally distribute datacenters across an Internet Service Provider (ISP) network.

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for Over-The-Top video streaming. A HAS video consists of multiple segments, encoded at multiple quality levels. Allowing the client to select the quality level for every segment, a smoother playback and a higher Quality of Experience (QoE) can be perceived. Although results are promising, current quality selection heuristics are generally hard coded. Fixed parameter values are used to provide an acceptable QoE under all circumstances, resulting in suboptimal solutions. Furthermore, many commercial HAS implementations focus on a video-on-demand scenario, where a large buffer size is used to avoid play-out freezes. When the focus is on a live TV scenario however, a low buffer size is typically preferred, as the video play-out delay should be as low as possible. Hard coded implementations using a fixed buffer size are not capable of dealing with both scenarios. In this paper, the concept of reinforcement learning is introduced at client side, allowing to adaptively change the parameter configuration for existing rate adaptation heuristics. Bandwidth characteristics are taken into account in the decision process, thus allowing to improve the client's bandwidth-awareness. Focus in this paper is on actively reducing the average buffer filling, evaluating results for two heuristics: the Microsoft IIS Smooth Streaming heuristic and the QoE-driven Rate Adaptation Heuristic for Adaptive video Streaming by Petrangeli et al. We show that using the proposed learning-based approach, the average buffer filling can be reduced by 8.3% compared to state of the art, while achieving a comparable level of QoE.

Cloud computing is a technology that enables elastic, on-demand resource provisioning. Migrating applications to the cloud can increase their elasticity, allowing them to adapt to workload changes by dynamically allocating resources. In a multi-tenant application multiple client organizations, each referred to as tenants, make use of one or more shared application instances. These shared instances must however behave like a private instance by guaranteeing both data separation and performance isolation for every tenant. In order to achieve high scalability, a multi-tenant application running on the elastic cloud requires a flexible and scalable architecture for both the computational resources and the storage resources. In this paper we present and evaluate the design of a data management framework which can be used to extend existing multi-tenant cloud applications in order to achieve high scalability of the storage resources. We describe the most important components, and discuss important design choices. The framework invokes data allocation algorithms in order to find a feasible allocation of tenant data resulting in a minimal operating cost and a maximal performance, while taking no more than 10 ms to execute.

To realize the Internet of Things (IoT) vision, tools are needed to ease the development and deployment of practical applications. Several standard bodies, companies, and ad-hoc consortia are proposing their own solution for inter-device communication. In this context, DYnamic, Adaptive MAnagement of Networks and Devices (DYAMAND) was presented in a previous publication to solve the interoperability issues introduced by the multitude of available technologies. In this paper a DYAMAND case study is presented: in cooperation with a large company, a monitoring application was developed for flexible office spaces in order to reliably reorganize an office environment and give real-time feedback on the usage of meeting rooms. Three wireless sensor technologies were investigated to be used in the pilot. The solution was deployed in a "friendly user" setting at a research institute (iMinds) prior to deployment at the large company's premises. Based on the findings of both installations, requirements for an application platform supporting development and management of smart (office) applications were listed. DYAMAND was used as the basis of the implementation. Although the local management of networked devices as provided by DYAMAND enables easier development of intelligent applications, a number of remote services discussed in this paper are needed to enable reliable and up-to-date support (of new technologies).

The huge diffusion of mobile devices is rapidly changing the way multimedia content is consumed. Mobile devices are often used as a second screen, providing complementary information on the content shown on the primary screen, as different camera angles in case of a sport event. The introduction of multiple camera angles poses many challenges with respect to guaranteeing a high Quality of Experience to the end user, especially when the live aspect, different devices and highly variable network conditions typical of mobile environments come into play. Due to the ability of HTTP Adaptive Streaming (HAS) protocols to dynamically adapt to bandwidth fluctuations, they are especially suited for the delivery of multimedia content in mobile environments. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. Recently, a standardized solution has been proposed by the MPEG consortium, called Dynamic Adaptive Streaming over HTTP (DASH). We present in this paper a DASH-compliant iOS video player designed to support research on rate adaptation heuristics for live second screen scenarios in mobile environments. The video player allows to monitor the battery consumption and CPU usage of the mobile device and to provide this information to the heuristic. Live and Video-on-Demand streaming scenarios and real-time multi-video switching are supported as well. Quantitative results based on real 3G traces are reported on how the developed prototype has been used to benchmark two existing heuristics and to analyse the main aspects affecting battery lifetime in mobile video streaming.

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services over the Internet. In HAS, each video is segmented and stored in different qualities. Rate adaptation heuristics, deployed at the client, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. Current heuristics under-perform when sudden bandwidth drops occur, therefore leading to freezes in the video play-out, the main factor influencing users' Quality of Experience (QoE). In this article, we propose an Openflow-based framework capable of increasing clients' QoE by reducing video freezes. An Openflow-controller is in charge of introducing prioritized delivery of HAS segments, based on feedback collected from both the network nodes and the clients. To reduce the side-effects introduced by prioritization on the bandwidth estimation of the clients, we introduce a novel mechanism to inform the clients about the prioritization status of the downloaded segments without introducing overhead into the network. This information is then used to correct the estimated bandwidth in case of prioritized delivery. By evaluating this novel approach through emulation, under varying network conditions and in several multi-client scenarios, we show how the proposed approach can reduce freezes up to 75% compared to state-of-the-art heuristics.

Vendor lock-in is one of the major issues preventing companies from moving their big data applications to the cloud or changing between cloud providers. A choice in provider based on used datastores can be advantageous at first, but with ever-changing applications the chosen datastore may no longer be optimal after some time. Namely, applications' requirements change due to frequent updates and feature requests, and scalability issues arise as user numbers continuously evolve. In this paper we propose a framework for the live transformation of the schema and data of datastores. Using a canonical data model the framework can be easily extended for additional datastores. The framework performs the transformation on two different levels. It uses a batch layer to transform a snapshot of the datastore, while a speed layer transforms queries inserting new or updated data into the datastore. A transformation is given between MySQL and Cassandra as a proof-of-concept. We show the correctness of the transformation and provide performance results, in terms of transformation times and overhead.

Although geometric routing is proposed as a memory-efficient alternative to traditional lookup-based routing and forwarding algorithms, it still lacks: (i) adequate mechanisms to trade stretch against load balancing, and (ii) robustness to cope with network topology change. The main contribution of this paper involves the proposal of a family of routing schemes, called Forest Routing. These are based on the principles of geometric routing, adding flexibility in its load balancing characteristics. This is achieved by using an aggregation of greedy embeddings along with a configurable distance function. Incorporating link load information in the forwarding layer enables load balancing behavior while still attaining low path stretch. In addition, the proposed schemes are validated regarding their resilience towards network failures.

HTTP Adaptive Streaming (HAS) is today the number one video technology for over-the-top video distribution. In HAS, video content is temporally divided into multiple segments and encoded at different quality levels. A client selects and retrieves per segment the most suited quality version to create a seamless playout. Despite the ability of HAS to deal with changing network conditions, HAS-based live streaming often suffers from freezes in the playout due to buffer under-run, low average quality, large camera-to-display delay, and large initial/channel-change delay. Recently, IETF has standardized HTTP/2, a new version of the HTTP protocol that provides new features for reducing the page load time in Web browsing. In this paper, we present ten novel HTTP/2-based methods to improve the quality of experience of HAS. Our main contribution is the design and evaluation of a push-based approach for live streaming in which super-short segments are pushed from server to client as soon as they become available. We show that with an RTT of 300 ms, this approach can reduce the average server-to-display delay by 90.1% and the average start-up delay by 40.1%.

Network function virtualization has received attention from both academia and industry as an important shift in the deployment of telecommunication networks and services. It is being proposed as a path towards cost efficiency, reduced time-to-markets, and enhanced innovativeness in telecommunication service provisioning. However, efficiently running virtualized services is not trivial as, among other initialization steps, it requires first mapping virtual networks onto physical networks, and thereafter mapping and scheduling virtual functions onto the virtual networks. This paper formulates the online virtual function mapping and scheduling problem and proposes a set of algorithms for solving it. Our main objective is to propose simple algorithms that may be used as a basis for future work in this area. To this end, we propose three greedy algorithms and a tabu search-based heuristic. We carry out evaluations of these algorithms considering parameters such as successful service mappings, total service processing times, revenue, cost etc, under varying network conditions. Simulations show that the tabu search-based algorithm performs only slightly better than the best greedy algorithm.

Geometric routing is an alternative to traditional routing algorithms in which traffic is no longer forwarded using lookup tables, but using coordinates in an embedding of the underlying network. A major downside of current geometric routing algorithms is their inability to handle network failures in a graceful manner. Moreover, they cannot deal with dynamic graph topologies. This article presents a geometric routing scheme that uses an embedding based on a spanning forest. Allowing nodes to select the optimal spanning tree leads to both shorter paths and natural traffic redirection in case of network failures. By constructing the forest in such a way that its disconnected components have low redundancy, their coverage is maximized. Results show that this system is able to operate gracefully in severe failure scenarios, without any form of path protection or restoration. By means of an embedding regeneration procedure, the routing scheme is able to continuously adapt to an altering network topology. This geometric routing algorithm effectively combines two key objectives, namely low path stretch and high robustness. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 66(4), 320–334 2015

The articles in this special section focus on the evolution of software defined networking; network virtualization; and network function virtualization.

In Part I of the special issue, the main reported research contributions were: efficient resource allocation and management of softwarized network functions, design of highperformance platforms to allow network function virtualization on commodity machines, and enabling efficient collaboration between providers in softwarized networks. From the twenty six submitted papers, four papers had been selected for Part I of the special issue. An additional set of four more papers have been accepted for this Part II, after a thorough revision by the authors to take into account the detailed comments from the reviewers. The four selected papers in Part II of the special address three very important topics for the efficient management of Software-Defined Networking/Virtualized Network Functions-based (SDN/NFV-based) telecommunication systems: (i) optimizations to flow-based software-defined networks to address the scalability and energy consolidation requirements, (ii) programming abstractions in wireless software-defined networks, and (iii) improved network virtualization to more efficiently support latency sensitive applications.

Current nurse call systems hinder the efficiency of nurses as the systems are not aware of the type of requested help and the context in which their help is required. To tackle these issues, we have developed an ontology-based nurse call system that automatically takes the patients' and caregivers' profiles and context into account when assigning calls to nurses by modelling this information in an ontology, i.e., a formal domain model. For example, current tasks of the nurses and trust relationship with patients are considered while allocating calls to caregivers. Focus is not only on creating a higher quality patient care, but also on distributing the workload more evenly over all caregivers. However, not in all hospital departments such a smart nurse call system will have a significant impact, e.g., geriatric versus emergency care. To gain insights into the total impact of a smart nurse call system, a dedicated discrete event simulation (DES) model is presented that tests its performance. Based on realistic nurse call logs and information gathered at representative hospital departments through interviews and observations, the simulation model allows optimizing decisions, modelled as rules based on the information captured in the ontology, to allocate calls to the best suited nurse. Several scenarios with a varying number of calls, staff members, etc. are tested to be able to define the effectiveness and the (dis)advantages of the ontology-based system with respect to the current one. In conclusion, recommendations are made towards improving the currently employed nurse call systems in hospitals.

Security is not taken into account by default in the Representational State Transfer (REST) architecture, but its layered architecture provides many opportunities for implementing it. In this paper, a security mechanism for Web Service communication through mobile clients devices is proposed, that conforms to the REST architecture as much as possible. This approach has been inspired by some known security mechanisms, but implemented in such a way that it focusses on statelessness and aims to be lightweight. Results indicate that the custom security mechanism outperforms the Transport Layered Security (TLS) based system. Because of the genericness of REST, the proposed security mechanism can be adopted by a wide variety of other RESTful Web Services.

Cloud environments face a growing demand for application hosting, and applications consisting of multiple data-sources and storage components. The need to ensure service level agreements for these types of applications creates important challenges for cloud infrastructure providers. The main contribution of this paper is an optimal cost-effective model and two algorithms to map component-based data oriented applications to cloud platforms. The first algorithm is based on an Integer Linear Programming formulation and minimizes an objective function, taking into account the capacities of the available nodes and links, as well as the customer requirements. This algorithm is able to obtain the optimal solution, but shows a limited scalability. For this reason a heuristic algorithm is designed to solve the scalability issue. The experimental results thoroughly compare the execution times and obtained node usage for both algorithms.

Over the past decades, the importance of multimedia services such as video streaming has increased considerably. Since streaming protocols such as Real Time Streaming Protocol (RTSP) and Real Time Transport Protocol (RTP) require server-side bit-rate adaptation schemes, they are not ideally suited to deal with highly heterogeneous and dynamically changing network conditions. Therefore, research shifted towards client-side adaptation schemes, requiring significantly less investments in server-side infrastructure. HTTP Adaptive Streaming (HAS) is now becoming omnipresent in video streaming services due to many advantages offered by HTTP-based streaming: reliable transmission over TCP, reuse of existing caching infrastructure and compatibility with NATs and firewalls. In HAS, the video content is split temporally into segments which are encoded at different quality rates. The client side heuristic decides at which quality rate each segment should be downloaded, based on measured network statistics, buffer filling level and device characteristics. Traditionally, Advanced Video Coding (AVC) is used to encode the different segments, introducing a significant amount of redundancy across quality representations. Scalable Video Coding (SVC) can cope with these issues of content redundancy by creating dependencies between the base and enhancement layers. Adopting SVC in HAS significantly improves caching and bandwidth efficiency at the server side. Another advantage of SVC, is the ability to gradually upgrade the quality of the video by downloading additional video layers.

HTTP Adaptive Streaming (HAS) has several advantages compared to traditional streaming protocols, such as easy traversal of firewalls and reuse of widely deployed HTTP infrastructure. HAS content is temporally segmented, and encoded at different quality representations, allowing the video player to autonomously adapt to network conditions by adapting play-out quality between subsequent segment downloads. However, to guarantee continuous playback, current-generation HAS protocols require a large play-out buffer. This makes them ill-suited for live television, as it significantly increases the live signal delay. This paper proposes a novel HAS solution for live streaming services. A HAS video player was designed that can cope with buffers as small as 2 seconds. This obviously requires the player to more rapidly react to bandwidth changes, which was achieved by using the Scalable Video Coding (SVC) extension of the H.264 Advanced Video Coding (AVC) video codec. Moreover, an intelligent network proxy was developed that guarantees the delivery of the SVC base quality layer using Differentiated Services (DiffServ). Furthermore, a more dynamic deadline-based approach is proposed which allows the client itself to decide which segments should be prioritized based on the risk of running into a buffer starvation. This enables more efficient use of the prioritized channel, leading to less freezes and increased quality and stability. The combination of these technologies allows the video player to align its quality adaptation decisions to the available bandwidth more efficiently and completely avoid buffer starvations. The small buffer size also reduces the total live signal delay from multiple dozens to only a few seconds.

In recent years, HTTP Adaptive Streaming (HAS) is becoming the de facto standard for video delivery over the best effort Internet. In HAS, the video consists out of multiple temporal segments encoded at different quality rates. In this way, HAS allows to dynamically adapt the quality level to the perceived network conditions. Using Scalable Video Coding (SVC), the redundancy between these representations can be eliminated, increasing the efficiency of server and caching infrastructure. Software Defined Networking (SDN) allows the dynamic adjustment of forwarding tables to reroute different flows. Using a combination of the layered characteristics of SVC and the dynamic routing of flows, the delivery of video can be optimized. In this paper, an algorithm is presented to dynamically calculate the optimal delivery paths for the different video layers. This enables guaranteeing a reliable and continuous video playout. Using this approach the number of video freezes can be reduced with 72% compared to shortest path routing.

Multi-tenancy leads to improved efficiency, improved scalability, and lower costs. With the recent evolution of Cloud Computing and Software-as-a-Service (SaaS) in particular, a flexible and scalable multi-tenant architecture is becoming highly important. In multi-tenant applications, each tenant has its own users and administrators and tenants even tend to be divided into multiple subtenants. As the number of tenants grows, the number of users and amount of data grows, thus a scalable architecture for the access control system is needed. The question arises how to distribute the users and data over multiple database instances. In this paper we present a hierarchical data management approach, taking performance metrics into account, for structuring the storage of tenant data in large multi-tenant environments. We introduce a logical representation of the tenants, the tenant tree, and make a mapping to the physical storage by introducing three models for load-balancing. Next, we focus on how to efficiently locate the required data and introduce multiple search approaches. We characterize the impact on the performance both theoretically and experimentally. Experiments confirm that the theoretical analysis is in line with the experimental results. When the amount of data increases significantly, dividing the data over multiple datastores in an efficient way will eliminate the overhead and lead to a performance gain, especially if most of the data is located at the leaf nodes of the tenant tree.

Network virtualisation is considerably gaining attention as a solution to ossification of the Internet. However, the success of network virtualisation will depend in part on how efficiently the virtual networks utilise substrate network resources. In this paper, we propose a machine learning-based approach to virtual network resource management. We propose to model the substrate network as a decentralised system and introduce a learning algorithm in each substrate node and substrate link, providing self-organization capabilities. We propose a multiagent learning algorithm that carries out the substrate network resource management in a coordinated and decentralised way. The task of these agents is to use evaluative feedback to learn an optimal policy so as to dynamically allocate network resources to virtual nodes and links. The agents ensure that while the virtual networks have the resources they need at any given time, only the required resources are reserved for this purpose. Simulations show that our dynamic approach significantly improves the virtual network acceptance ratio and the maximum number of accepted virtual network requests at any time while ensuring that virtual network quality of service requirements such as packet drop rate and virtual link delay are not affected.

In cloud environments, resources can be requested on-demand when they are needed. A cloud management system is responsible for determining which physical machines are responsible for processing the requests. The problem of determining which servers are used for which services is referred to as the Cloud Application Placement Problem (CAPP), and multiple criteria such as cost and number of migrations must be taken into account. When applications are constructed as a collection of communicating services, such as in Service-Oriented Architectures, it becomes important to take the underlying network properties into account when these placement decisions are made. In this paper, we propose an Integer Linear Programming (ILP) formulation for the CAPP, which optimizes multiple criteria such as cost, latency and number of migrations between subsequent invocations by using multiple optimization criteria. We also present hierarchical algorithms based on particle swarm optimization and genetic algorithms to solve the CAPP. These algorithms are be executed within a management hierarchy, which reduces the amount of information needed for the algorithms to function, increasing scalability of the management system. Finally, we evaluate the hierarchical algorithms by comparing them to an optimal algorithm based on the ILP formulation.

HTTP Adaptive Streaming (HAS) is quickly becoming the de facto standard for Over-The-Top video streaming. In HAS, each video is temporally segmented and stored in different quality levels. Quality selection heuristics, deployed at the video player, allow dynamically requesting the most appropriate quality level based on the current network conditions. Today's heuristics are deterministic and static, and thus not able to perform well under highly dynamic network conditions. Moreover, in a multi-client scenario, issues concerning fairness among clients arise, meaning that different clients negatively influence each other as they compete for the same bandwidth. In this article, we propose a Reinforcement Learning-based quality selection algorithm able to achieve fairness in a multi-client setting. A key element of this approach is a coordination proxy in charge of facilitating the coordination among clients. The strength of this approach is three-fold. First, the algorithm is able to learn and adapt its policy depending on network conditions, unlike current HAS heuristics. Second, fairness is achieved without explicit communication among agents and thus no significant overhead is introduced into the network. Third, no modifications to the standard HAS architecture are required. By evaluating this novel approach through simulations, under mutable network conditions and in several multi-client scenarios, we are able to show how the proposed approach can improve system fairness up to 60% compared to current HAS heuristics.

Data is abundantly present in today's world and the amount of data we generate continues to grow. The representation and structure of this data, however, differs greatly depending on the software or platform. The wide variety of software available shows there is no one optimal way to model data for all software, but when you want to deploy software in the cloud using a Platform-as-a-Service (PaaS) provider, you are restricted to the data model chosen by the provider. We propose Kameleo, a new Platform-as-a-Service able to support several data models. This provides software developed by the customers with an optimal data model even when the requirements of the software change. In order to evaluate the platform, we compared three multitenancy models using a basic webshop application. Results show a clear difference in performance between the models.

In recent years, there has been a rising interest in cloud computing, which is often used to offer Software as a Service (SaaS) over the Internet. SaaS applications can be offered to clients at a lower cost as they are usually multi-tenant: many end users make use of a single application instance, even when they are from different organisations. It is difficult to offer highly customizable SaaS applications that are still multi-tenant, which is why these SaaS applications are often offered in a one size fits all approach.

In recent years, HTTP (Hypertext Transfer Protocol) adaptive streaming (HAS) has become the de facto standard for adaptive video streaming services. A HAS video consists of multiple segments, encoded at multiple quality levels. State-of-the-art HAS clients employ deterministic heuristics to dynamically adapt the requested quality level based on the perceived network conditions. Current HAS client heuristics are, however, hardwired to fit specific network configurations, making them less flexible to fit a vast range of settings. In this article, a (frequency adjusted) Q-learning HAS client is proposed. In contrast to existing heuristics, the proposed HAS client dynamically learns the optimal behaviour corresponding to the current network environment in order to optimise the quality of experience. Furthermore, the client has been optimised both in terms of global performance and convergence speed. Thorough evaluations show that the proposed client can outperform deterministic algorithms by 11–18% in terms of mean opinion score in a wide range of network configurations.

HTTP adaptive streaming (HAS) services allow the quality of streaming video to be automatically adapted by the client application in face of network and device dynamics. Due to their advantages compared to traditional techniques, HAS-based protocols are widely used for over-the-top (OTT) video streaming. However, they are yet to be adopted in managed environments, such as ISP networks. A major obstacle is the purely client-driven design of current HAS approaches, which leads to excessive quality oscillations, suboptimal behavior, and the inability to enforce management policies. Moreover, the provider has no control over the quality that is provided, which is essential when offering a managed service. This article tackles these challenges and facilitates the adoption of HAS in managed networks. Specifically, several centralized and distributed algorithms and heuristics are proposed that allow nodes inside the network to steer the HAS client's quality selection process. The algorithms are able to enforce management policies by limiting the set of available qualities for specific clients. Additionally, simulation results show that by coordinating the quality selection process across multiple clients, the proposed algorithms significantly reduce quality oscillations by a factor of five and increase the average delivered video quality by at least 14%.

In recent years, the scale of clouds and networks has increased greatly. It is important to ensure that the management systems used in these environments can scale as well. A centralized system does not scale well, while for distributed approaches, it is difficult to maintain an overview of the global system state. In hierarchical management systems, nodes at a low level in the hierarchy have a detailed view of a small part of the network, while higher-level nodes have a less detailed view of larger parts of the network. This makes hierarchical management systems well suited for large scale systems. The structure of such a hierarchical system should however be impacted by the management system for which it is used, as various properties such as the number of child nodes, tree depth and the distance between nodes can impact the performance of the management system. In this paper, we describe the Scalable Hierarchical Management Framework (SHMF), a scalable approach for constructing a hierarchical management system, suitable for large-scale cloud environments, that automatically optimizes its structure in function of its overlying management system. We evaluate the approach based on the requirements for the cloud application placement problem.

Emergency response applications require the processing of large amounts of data, generated by a diverse set of sensors and devices, in order to provide for an accurate and concise view of the situation at hand. The adoption of semantic technologies allows for the definition of a formal domain model and intelligent data processing and reasoning on this model based on generated device and sensor measurements. This paper presents a novel approach to emergency response applications, such as fire fighting, integrating a formal semantic domain model into an event-based decision support system, which supports reasoning on this model. The developed model consists of several generic ontologies describing concepts and properties which can be applied to diverse context-aware applications. These are extended with emergency response specific ontologies. Additionally, inference on the model performed by a reasoning engine is dynamically synchronized with the rest of the architectural components. This allows to automatically trigger events based on predefined conditions. The proposed ontology and developed reasoning methodology is validated on two scenarios, i.e. (i) the construction of an emergency response incident and corresponding scenario and (ii) monitoring of the state of a fire fighter during an emergency response.

Having numerous sensors on-board, smartphones have rapidly become a very attractive platform for augmented reality applications. Although the computational resources of mobile devices grow, they still cannot match commonly available desktop hardware, which results in downscaled versions of well known computer vision techniques that sacrifice accuracy for speed. We propose a component-based approach towards mobile augmented reality applications, where components can be configured and distributed at runtime, resulting in a performance increase by offloading CPU intensive tasks to a server in the network. By sharing distributed components between multiple users, collaborative AR applications can easily be developed. In this poster, we present a component-based implementation of the Parallel Tracking And Mapping (PTAM) algorithm, enabling to distribute components to achieve a mobile, distributed version of the original PTAM algorithm, as well as a collaborative scenario.

The consumption of multimedia services over HTTP-based delivery mechanisms has recently gained popularity due to their increased flexibility and reliability. Traditional broadcast TV channels are now offered over the Internet, in order to support Live TV for a broad range of consumer devices. Moreover, service providers can greatly benefit from offering external live content (e.g., YouTube, Hulu) in a managed way. Recently, HTTP Adaptive Streaming (HAS) techniques have been proposed in which video clients dynamically adapt their requested video quality level based on the current network and device state. Unlike linear TV, traditional HTTP- and HAS-based video streaming services depend on unicast sessions, leading to a network traffic load proportional to the number of multimedia consumers. In this paper we propose a novel HAS-based video delivery architecture, which features intelligent multicasting and caching in order to decrease the required bandwidth considerably in a Live TV scenario. Furthermore we discuss the autonomic selection of multicasted content to support Video on Demand (VoD) sessions. Experiments were conducted on a large scale and realistic emulation environment and compared with a traditional HAS-based media delivery setup using only unicast connections.

The Internet is rapidly evolving towards a multimedia service delivery platform. However, existing Internet-based content delivery approaches have several disadvantages, such as the lack of Quality of Service (QoS) guarantees. Future Internet research has presented several promising ideas to solve the issues related to the current Internet, such as federations across network domains and end-to-end QoS reservations. This paper presents an architecture for the delivery of multimedia content across the Internet, based on these novel principles. It facilitates the collaboration between the stakeholders involved in the content delivery process, allowing them to set up loosely-coupled federations. More specifically, the Federated Resource Reservation (FedRR) algorithm is proposed. It identifies suitable federation partners, selects end-to-end paths between content providers and their customers, and optimally configures intermediary network and infrastructure resources in order to satisfy the requested QoS requirements and minimize delivery costs.

Recently, the Internet has become a popular platform for the delivery of multimedia content. However, its best effort delivery approach is ill-suited to guarantee the stringent Quality of Service (QoS) requirements of many existing multimedia services, which results in a significant reduction of the Quality of Experience. This paper presents a solution to these problems, in the form of a framework for dynamically setting up federations between the stakeholders involved in the content delivery chain. More specifically, the framework provides an automated mechanism to set up end-to-end delivery paths from the content provider to the access Internet Service Providers (ISPs), which act as its direct customers and represent a group of end-users. Driven by Service Level Agreements (SLAs), QoS contracts are automatically negotiated between the content provider, the access ISPs, and the intermediary network domains along the delivery paths. These contracts capture the delivered QoS and resource reservation costs, which are subsequently used in the price negotiations between content provider and access ISPs. Additionally, it supports the inclusion of cloud providers within the federations, supporting on-the-fly allocation of computational and storage resources. This allows the automatic deployment and configuration of proxy caches along the delivery paths, which potentially reduce delivery costs and increase delivered quality.

The proliferation of multimedia services over access networks (e.g., IPTV or network-based Personal Video Recording) has introduced important new revenue potential for network and service providers but has also complicated the management burden. As a result, today's management of multimedia networks is often too static to cope with the increasing quality requirements of multimedia services. A key point in these quality requirements is the quality as perceived by the end users, denoted as the Quality of Experience (QoE). In the thesis, we have introduced an auto-nomic management layer that optimizes the QoE of multimedia networks. We have studied several QoE optimizing techniques with respect to traffic adaptation, admission control and video rate adaptation. All these QoE optimizing techniques exhibit autonomic behavior as they continuously monitor the network to optimize their configuration and consequently optimize the QoE. Furthermore, we have investigated the coordinated deployment of these QoE optimizing techniques by focusing on the exchange of context between entities in the distributed autonomic management layer. Through extensive evaluation using both simulation and emulation on a large-scale testbed, we have shown that the proposed QoE optimizing techniques can successfully optimize the QoE of multimedia services. This QoE optimization was characterized in terms of metrics such as the number of admitted sessions and video quality.

Distributed renewable power generators, such as solar cells and wind turbines are difficult to predict, making the demand-supply problem more complex than in the traditional energy production scenario. They also introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. In this article we describe a distributed algorithm for residential energy management in smart power grids. This algorithm consists of a market-oriented multi-agent system using virtual energy prices, levels of renewable energy in the real-time production mix, and historical price information, to achieve a shifting of loads to periods with a high production of renewable energy. Evaluations in our smart grid simulator for three scenarios show that the designed algorithm is capable of improving the self consumption of renewable energy in a residential area and reducing the average and peak loads for externally supplied power.

While the use of cloud computing is on the rise, many obstacles to its adoption remain. One of the weaknesses of current cloud offerings is the difficulty of developing highly customizable applications while retaining the increased scalability and lower cost offered by the multi-tenant nature of cloud applications. In this paper we describe a Software Product Line Engineering (SPLE) approach to the modelling and deployment of customizable Software as a Service (SaaS) applications. Afterwards we define a formal feature placement problem to manage these applications, and compare several heuristic approaches to solve the problem. The scalability and performance of the algorithms is investigated in detail. Our experiments show that the heuristics scale and perform well for systems with a reasonable load.

In recent years, there has been a growing interest in cloud technologies. Using current cloud solutions, it is however difficult to create customizable multi-tenant applications, especially if the application must support varying Quality of Service (QoS) guarantees. Software Product Line Engineering (SPLE) and feature modeling techniques are commonly used to address these issues in non-cloud applications, but these techniques cannot be ported directly to a cloud context, as the common approaches are geared towards customization of on-premise deployed applications, and do not support multi-tenancy. In this paper, we propose an architecture for the development and management of customizable Software as a Service (SaaS) applications, built using SPLE techniques. In our approach, each application is a composition of services, where individual services correspond to specific application functionalities, referred to as features. A feature-based methodology is described to abstract and convert the application information required at different stages of the application life-cycle: development, customization and deployment. We specifically focus on how development feature models can be adapted ensuring a one-to-one correspondence between features and services exists, ensuring the composition of services yields an application containing the corresponding features. These runtime features can then be managed using feature placement techniques. The proposed approach enables developers to define significantly less features, while limiting the amount of automatically generated features in the application runtime stage. Conversion times between models are shown to be in the order of milliseconds, while execution times of management algorithms are shown to improve by 5 to 17% depending on the application case.

Nowadays, public as well as private communication infrastructures are all contending for the same limited amount of bandwidth. To optimally share network resources, symbiotic networks have been proposed, which cross logical and physical boundaries to improve the reliability, scalability, and energy efficiency of the network as a whole as well as its constituents. This paper focuses on software services in such symbiotic networks. We propose a platform for the intelligent composition of services provided by symbiotically connected parties, resulting in novel cooperation opportunities. The platform harvests Semantic Web technology to describe services in a highly expressive manner, and constructs service compositions using SeCoA, our tunable best-first search algorithm. The resulting compositions are then enacted via CaPI, a reconfigurable middleware infrastructure. By means of an illustrative scenario, we provide further insight into the platform's functioning.

We propose symbiotic networks, a novel approach toward sharing of network resources in order to increase the scalability, dependability and energy efficiency of colocated networks. As symbiotic networks offer large amounts of software services, one challenge is to allow these services to operate “symbiotically” as well. By combining services from different parties, service compositions arise, which allow for a richer set of functionality. Creating such compositions, however, requires intricate knowledge about services and their interoperability. Using a semantic domain and service model, we describe SeCoA, a tunable best-first search algorithm for autonomously constructing symbiotic service compositions. A performance evaluation of SeCoA was conducted, showing that the algorithm offers acceptable performance for moderately sized compositions.

In recent years, the networking community has put a significant research effort in identifying new ways to distribute content to multiple users in a better-than-unicast manner. Scalable delivery is more important now video is the dominant traffic type and further growth is expected. To make content distribution scalable, in-network optimization functions are needed such as caches. The established transport layer protocols are end-to-end and do not allow optimizing transport below the application layer, hence the popularity of overlay application layer solutions located in the network. In this paper, we introduce a novel transport protocol, the Shared Content Addressing Protocol (SCAP) that allows in-network intermediate elements to participate in optimizing the delivery process, using only the transport layer. SCAP runs on top of standard IP networks, and SCAP optimization functions can be plugged-in the network transparently as needed. As such, only transport protocol based intermediate functions need to be deployed in the network, and the applications can stay at the topological end points. We define and evaluate a prototype version of the SCAP protocol using both simulation and a prototype implementation of a transparent SCAP-only intermediate optimization function.

The increase of distributed renewable electricity generators, such as solar cells and wind turbines, requires new energy management systems where real-time measurements and communication between end users, suppliers and utilities are vital. To address this need, we propose a common service architecture that allows houses with renewable energy generation and smart energy devices to plug into a distributed energy management system, integrated with the public power grid. The presented architecture facilitates end-users to optimize their energy consumption, enables power network operators to better balance supply and demand, and creates a platform where new market players (e.g. ESCOs) can easily provide new services. This service architecture has been implemented and is currently evaluated in a field trial with 21 users, of which we present the initial results.

The evolution toward grid and cloud computing as observed for over a decennium illustrates the crucial role played by (optical) networks in supporting today's applications. In this paper, we start from an overview of the challenging applications in both academic (further referred to as scientific), enterprise (business) and nonprofessional user (consumer) domains. They pose novel challenges, calling for efficient interworking of IT resources, for both processing and storage, as well as the network that interconnects them and provides access to their users. We outline those novel applications' requirements, including sheer performance attributes (which will determine the quality as perceived by end users of the cloud applications), as well as the ability to adapt to changing demands (usually referred to as elasticity) and possible failures (i.e., resilience). In outlining the foundational concepts that provide the building blocks for grid/cloud solutions that meet the stringent application requirements we highlight, a prominent role is played by optical networking. The pieces of the solution studied in this respect span the optical transport layer as well as mechanisms located in higher layers (e.g., anycast routing, virtualization) and their interworking (e.g., through appropriate control plane extensions and middleware). Based on this study, we conclude by identifying challenges and research opportunities that can enable future-proof optical cloud systems (e.g., pushing the virtualization paradigms to optical networks).

In recent years, telecom operators have been moving away from traditional, broadcast-driven, television towards IP-based, interactive and on-demand services. Consequently, multicast is no longer a viable solution to limit the amount of traffic in the IP-TV network. In order to counter an explosion in generated traffic, caches can be strategically placed throughout the content delivery infrastructure. As the size of caches is usually limited to only a small fraction of the total size of all content items, it is important to accurately predict future content popularity. Classical caching strategies only take into account the past when deciding what content to cache. Recently, a trend towards novel strategies that actually try to predict future content popularity has arisen. In this paper, we ascertain the viability of using popularity prediction in realistic multimedia content caching scenarios. The use of popularity prediction is compared to classical strategies using trace files from an actual deployed Video on Demand service. Additionally, the synergy between several parameters, such as cache size and prediction window, is investigated.

As the requirements and scale of cloud environments increase, scalable management of the cloud is needed. Centralized solutions lack scalability and fully distributed management systems only have a limited overview of the system. One of the often-studied problems in cloud environments is the application placement problem, used to decide where application instances are instantiated and how many resources to allocate to the instances. In this paper a general approach is introduced for using centralized cloud resource management algorithms in a hierarchical context, increasing the scalability of the management system while maintaining a high placement quality. The management system itself is executed on the cloud, further increasing scalability and robustness. The proposed method uses aggregation techniques to generate input values for a centralized application placement algorithm which is run in all management nodes. Decoupling ensures management nodes can function independently. Subsequently, we compare the performance of hierarchical application placement method with that of a fully centralized algorithm. The results show that a solution, within 5% of the optimum placement when using the centralized algorithm, can be achieved hierarchically in less than 25% of the time needed for execution of the centralized algorithm.

We propose a software platform which pairs context awareness with over-the-top (OTT) service deployment. By augmenting OTT services with local context information, we allow them to react upon various types of changes in the environment in which they are being deployed. This lets service providers offer more personalized and fine-grained applications, while making use of a third-party infrastructure, via the OTT paradigm. Through UML diagrams, we describe the architecture of the proposed service platform. By means of a detailed illustrative scenario, the components involved are further clarified. In addition, in order to prove the feasibility of the architecture, a prototype implementation was developed and deployed on a large wireless sensor network test bed. Using a set of benchmarks, we identified the strengths and weaknesses of both test bed and prototype.

With the advent of novel services such as IPTV and videoconferencing broadband DSL networks are facing enormous challenges. These services have strict QoS demands in terms of packet loss, jitter and delay. In an effort to meet these demands, operators introduced centralized admission control mechanisms to avoid congestion when too many session were allowed. These centralized approaches often fail to effectively manage the available resources mainly because of the bursty nature of multimedia traffic. When transmitting variable bit rate videos resources are reserved based on the peak rate of the video. This leads to under-utilisation of the network. Measurement based admission control mechanism have been proposed such as the IETF Pre-Congestion Notification (PCN) to allow better network utilisation. Each node in the PCN domain measures the network load and admits or blocks accordingly sessions at the edges of the network. Previous research proposed bandwidth metering and an autonomic rate adaptation algorithm which led to a better utilisation of the network but still introduced unnecessary bandwidth headroom caused by the variable bit rate of videos. In this paper, we propose an additional buffering step before traffic enters the PCN domain and determine configuration guidelines for the parameters. The performance of this buffering step has been evaluated in an NS-2 based simulator environment. The conducted tests show a 26.5% increase of network utilisation.

The use of semantic technology has recently witnessed a huge increase. One of the areas in which this technology is being used increasingly more often is that of context-aware agents. However, the use of ontologies in general and reasoning in particular can rapidly become resource intensive. Certainly if the data set, called the A-Box, used by these agents grows considerably over time. Moreover, in order to create context-aware applications, taking into account a wide range of different data sets and context parameters, agents have to be provided to expose that data. The collaboration between the agents in the system is necessary to correlate the information and augment the intelligence and added value of the context-aware agents. Therefore, there is a need to have a distributed approach by means of a service-platform, where the different agents in a context-aware environment can collaborate. The main focus of this article is on the research on the design of a service-platform for semantic ontology-based context-aware collaboration. The platform architecture to allow the collaboration and scheduling, together with the associated algorithms, will be presented. The engineering and implementation details will be highlighted. By means of detailed UML sequence diagrams, we will present the workflow and collaboration between the different modules in the platform. Additionally, supporting developments, such as the meta-ontology and our ontology generator, OTAGen, will be presented. Furthermore, we will detail how the platform can operate in an autonomous way, taking into account the changing context of the agents in the platform.

The complexity and amount of medical information and data keeps increasing, which makes it difficult to maintain the same quality of care in the Intensive Care Unit, without significant cost increases. In order to contain this complexity, clinical guidelines are used to structure best practices and patient care, but they also support physicians and nurses in the diagnostic and treatment process. Currently, no standardized format exists to represent these guidelines. Moreover, they are often handwritten. Translating guidelines into a computer interpretable format can overcome problems in their workflow and improve clinician's uptake. To this end, we developed an automated generation and execution engine. Based on the requirements, both functional and non-functional, an architecture using the microkernel pattern is presented. This allows us to easily add and modify functionality. This architecture was evaluated with the guideline for the calculation of calorie need for burn patients, used on a daily basis in the Intensive Care Unit of the University Hospital of Ghent.

Medical time series contain important information about the condition of a patient. However, due to the large amount of data and the staff shortage, it is difficult for physicians to monitor these time series for trends that suggest a relevant clinical detoriation due to a complication or new pathology. This paper proposes a framework that supports physicians in detecting patterns in time series. It has three main tasks. First, the time-dependent data is gathered from heterogeneous sources and the semantics are made explicit by using an ontology. Second, Machine Learning techniques detect trends in the semantic time series data that indicate that a patient has a particular pathology. However, computerized classification techniques are not 100% accurate. Therefore, the third task consists of adding the pathology classification to the ontology with an associated probability and notifying the physician if necessary. The framework was evaluated with an ICU use case, namely detecting sepsis. Sepsis is the number one cause of death in the ICU.

A large number of intelligent components for managing the Future Internet have been proposed recently or are currently being investigated. However, before these network components can be deployed in real-life networks, they need to be thoroughly validated through realistic and large scale experiments. Testbed facilities provide a means to set up large scale network topologies but offer only a limited functionality in managing the deployment of the experiment itself. In this paper, we propose a management framework which automates the configuration and management of network experiments. The framework focuses on the emulation of user behaviour, to obtain realistic network conditions, and features several mechanisms that allow to reduce the experiments' size both temporally (in shorter simulation time) as spatially (with fewer physical nodes).

Clinical guidelines are used in the Intensive Care Unit to assist physicians and nurses in taking diagnostic or treatment decisions. Although these guidelines can be transformed into a computer executable format, they often are handwritten and not in a standardized format, which makes it difficult to convert them into working services. Moreover, manually translating guidelines can cause communication problems between software developers and the medical staff. Problems can also arise in the integration of clinical decision support into the clinical workflow and the uptake by doctors. To counter this, a modular, distributed, multi-tier framework was developed for translating guidelines into software applications and providing clinical decision support in the Intensive Care Unit. Different requirements were taken into account. The architecture has been implemented using Java Enterprise Edition. A service-oriented approach is used, allowing an easy introduction of new functionalities and integration with other systems. The architecture was evaluated with the antibiotic dosage guideline, which is used on a daily basis in the Intensive Care Unit.

The popularity of streaming multimedia services has greatly increased in recent years. Telco- and cable-providers have started offering a plethora of multimedia services in the access and aggregation network, including video on demand, interactive digital television, and time-shifted TV. However, these services introduce additional challenges, such as stringent time constraints, and high bandwidth requirements. To overcome these problems, we explore the advantages of delivering such multimedia content using deadline-aware scheduling and caching algorithms. These algorithms decide when to send and store which content. This enables the network to optimize bandwidth consumption and satisfy deadline constraints. The designed algorithm was evaluated and compared to classical deadline-unaware delivery protocols. This allows us to study the efficiency of the new algorithm, and identify the scenarios in which deadline-aware scheduling improves delivery of multimedia content.

Network management has suffered from increases in business, system, and operational complexity. This has been exacerbated by the heterogeneity in management data as well as the high quality requirements of multimedia services. Autonomic networking manages this growing complexity by adding intelligence inside network nodes and network management applications. While most autonomic applications simply use a control loop to monitor and configure entities, our work is aimed at building a self-governing network that is able to fulfill the requirements of current and future services. This means that management applications need a detailed and dynamic view of the contextual status of the network nodes as a whole in order to adapt their behaviour to changing context. In this paper, we propose an algorithm to semi-automatically generate filter rules based on existing information in a network management information model. These filter rules are used to determine the set of contextual data that needs to be exchanged with other nodes. The algorithm exploits the reasoning capabilities of ontologies and relies on the introduction of additional semantic relationships to achieve a fine-grained context exchange model. Large scale evaluations were conducted to characterise the performance of this ontological approach.

As the adoption of the Service Oriented Architecture paradigm has dramatically increased over the past few years, proper coordination of loosely coupled services becomes an important issue when building state-of-the-art applications. This coordination is typically organized through orchestration (requiring a central coordinating entity) or through choreographies. While the latter approach allows for a fully distributed coordination, the need also arises for a distributed conformance check, ensuring that each participant of the choreography behaves according to the general choreography. In this paper, a formalism is presented to ensure this conformance at design time, with possible extensions to deploy time and to runtime conformance checking. This formalism is referred to as the piX-model and it will be shown that the approach taken is inherently less complex, both in time and space, than the conventional π-calculus-based approach, whilst offering the same conformance guarantees. This gain in performance allows for a small design turnaround time, and also opens the avenue to runtime conformance checking by resource constrained devices.

As the variety in network service platforms and end user devices grows rapidly, content providers must constantly adapt their production system to support these new technologies. In this paper, we present a middleware platform for deploying highly interactive (television) applications over a diverse collection of networks and end user devices. As the user interface of such interactive applications may vary depending on the capabilities of the different target devices, our middleware uses UIML for the description of generic user interfaces. Our middleware platform also provides a pluggable support for new networks. A factor that highly complicates the design is the need for strict synchronization between an interactive application and video or audio data that is broadcasted. In order to support a maximum of functionality, downloadable application logic is used to provide the interactive services. As a test case, an evaluation setup was built, targeting both set-top boxes and mobile phones.

DSL aggregation networks are evolving to the standard platform for the delivery of multimedia services such as television and network based personal video recording. These multimedia services introduce large challenges for network operators as they are sensitive to packet loss. Therefore, admission control mechanisms are required to avoid congestion caused by allowing too many sessions. However, as multimedia services are often bursty it is not possible to reserve a fixed amount of bandwidth in the network since this policy will lead to either over-admittance or under-admittance. Recently, the IETF Pre-Congestion Notification (PCN) Working Group, proposed a measurement based admission control mechanism, where the network load is measured at each node and sessions are allowed or blocked at the edge of the network. In this paper, we extend and evaluate the PCN mechanism: we propose a new measurement algorithm for PCN, based on bandwidth metering, and determine the configuration guidelines for the parameters of both the original token bucket based approach and the novel algorithm for different network conditions and traffic types. More specifically, we study PCN's applicability on protecting VBR video services, which is currently not studied in the PCN Working Group. Furthermore, we characterise the gain of PCN in comparison to a centralised admission control mechanism.

This paper presents a dynamic composer for Web services. The services are enriched with semantic descriptions in OWL-S, based on which the composer automatically creates a combination of services reaching a specified goal. As an example, a trip planning use case is chosen where the goal ranges from booking of a single flight to planning of an entire trip including flight, hotel, transport, etc. The composition is achieved using local and global algorithms satisfying specific quality of service (QoS) constraints and requirements such as the execution time or cost of the invoked Web services. At the same time a more extended HTN planning algorithm is discussed, matching not only service outputs to inputs but also satisfying service preconditions through effects. In addition to the automatic composition, the paper also proposes a recovery mechanism in case of unavailable services. When executing the composition of flight services, unavailable services are dynamically replaced by equivalent services or a new composition achieving the needed result. The presented platform and planning algorithms are put through extensive performance and scalability tests for typical trip booking scenarios, in which basic services are composed to a complex trip planning service.

Service choreographies describe the interactions that take place in a distributed service collaboration without central entity orchestrating these interactions. It is obvious that each partner will execute parts of the choreography to fulfill the global collaborative effort. This paper focuses on translating the global choreography to local projections at design time. These projections need to be implemented by each participating partner. The process is decomposed in two steps: instantiation and extraction. In the instantiation step the abstraction levels are automatically determined, ranging from the choreography level to its smallest building blocks, the channel instances. In the extraction step, we present a way to map these channel instances to WS-BPEL. It is shown that this results in small WS-BPEL processes with a very straightforward correlation set, allowing for even resource-limited devices to participate in the choreography.

Massively online virtual environments (MOVEs) have been gaining popularity for several years. Today, these complex networked applications are serving thousands of clients simultaneously. However, these MOVEs are typically hosted on specialized server clusters and rely on internal knowledge of the services to optimize the load balancing. This makes running MOVEs an expensive undertaking as it cannot be outsourced to third party hosting providers. This paper details two integer linear programming approaches to optimize the MOVE deployment through load balancing and minimizing the delay experienced by the end-users. Optimization includes assigning MOVE components to resources and replication of components to increase the scalability. One approach assuming full application knowledge of a dedicated MOVE and one with no internal knowledge and geared toward a generic MOVE hosting platform. For both cases an optimizing heuristic is evaluated and the obtained results are compared.

A generic and self-managing service hosting infrastructure, provides a means to offer a large variety of services to users across the Internet. Such an infrastructure provides mechanisms to automatically allocate resources to services, discover the location of these services, and route client requests to a suitable service instance. In this paper we propose a dynamic and latency-aware algorithm for assigning resources to services. Additionally, the proposed service hosting architecture and its protocols to support the service placement algorithm, are described in detail. Extensive simulations were performed to compare the solution of our latency-aware algorithm to the latency-unaware variant, in terms of system efficiency and scalability.

In thin client computing, applications are executed on a network server instead of on the user terminal. Since the amount of processing at the terminal is reduced, thin clients are potentially energy efficient devices. However, a network connection between client and server is required for the transmission of user input and display updates. The energy needed for this intense network communication might undo or even exceed the power savings achieved by the reduction in client-side processing. In this paper, we present experimental results on power efficiency of the wireless platform on the thin client in case of thin client traffic. The discussion is focused on VNC-RFB, a widespread thin client protocol, over an IEEE 802.11 link in three typical user scenarios. The results indicate that a cross-layer approach between application and wireless link layer could potentially lead to important power savings.

A grid is a distributed computational and storage environment often composed of heterogeneous autonomously managed subsystems. As a result, varying resource availability becomes commonplace, often resulting in loss and delay of executing jobs. To ensure good grid performance, fault tolerance should be taken into account. Commonly utilized techniques for providing fault tolerance in distributed systems are periodic job checkpointing and replication. While very robust, both techniques can delay job execution if inappropriate checkpointing intervals and replica numbers are chosen. This paper introduces several heuristics that dynamically adapt the above mentioned parameters based on information on grid status to provide high job throughput in the presence of failure while reducing the system overhead. Furthermore, a novel fault-tolerant algorithm combining checkpointing and replication is presented. The proposed methods are evaluated in a newly developed grid simulation environment dynamic scheduling in distributed environments (DSiDE), which allows for easy modeling of dynamic system and job behavior. Simulations are run employing workload and system parameters derived from logs that were collected from several large-scale parallel production systems. Experiments have shown that adaptive approaches can considerably improve system performance, while the preference for one of the solutions depends on particular system characteristics, such as load, job submission patterns, and failure frequency.

In this paper, the focus is on how context information can be efficiently modeled with an ontology. This ontology can than be used by reasoning algorithms which are based on this context information. This is illustrated with a use case which studies the evolution from a place oriented to a person oriented nurse call system. An ontology was designed which holds the necessary context information. A nurse call algorithm that uses this information was constructed. The CASP Context framework was extended to implement the use case. This framework is bases on an OSGi framework. Rules are formulated to implement the algorithm. OWL was applied to integrate the ontology into the framework. A Web Service interface was designed which allows to insert new information into the Knowledge Base or extract information from it. At last a simulation was set up to show the advantages of the person oriented approach. The results of a performance study are shown as well.

On the one hand, agent-based software platforms are commonly used these days, while on the other hand Semantic Web technologies are also maturing. It is obvious that the combination of these two technologies can bring added value through the creation of Semantic Agent-based frameworks. However, it is also known that these Semantic Web technologies, and the reasoning on ontologies in particular, can rapidly become resource intensive. In order to get a clear view on this problem, we have developed OTAGen, a highly tunable tool to generate customized ontologies and corresponding queries. The generated ontologies can then be used to evaluate at design-time the performance of the Semantic Agent-based platform as a function of the number of ontologies, users and queries.

In this paper, an MILP-based methodology is presented that allows to optimize the deployment of a set of software components over a set of computing resources, with respect to fault tolerance and response times. The MILP model takes into account the reliability and performance parameters of hardware nodes and links, and optimizes a (configurable) trade-off between reliability and performance by replicating software components where necessary and finding an optimal deployment for them. The complete system can be modeled using UML component diagrams and activity diagrams, and an algorithm is presented to transform the UML model to the MILP model. The resulting deployment can then be fed back into the UML model. The applicability of the approach is demonstrated through a case study.

More and more, multimedia services are being accessed via fixed and mobile networks. These services are typically much more sensitive to packet loss, delay and/or congestion than traditional services. In particular, multimedia data is often time critical and, as a result, network issues are not well tolerated and significantly deteriorate the userpsilas quality of experience (QoE). We therefore propose a QoE optimization platform that is able to mitigate problems that might occur at any location in the delivery path from service provider to customer. More specifically, the distributed architecture supports overlay routing to circumvent erratic parts of the network core. In addition, it comprises proxy components that realize last mile optimization through automatic bandwidth management and the application of processing on multimedia flows. In this paper we introduce a transcoding service for this proxy component which enables the transformation of H.264/AVC video flows to an arbitrary bit rate. Through representative experimental results, we illustrate how this addition enhances the QoE optimization capabilities of the proposed platform by allowing the proxy component to compute more flexible and effective bandwidth distributions.

As the complexity of business processes grows, the shift towards workflow-based programming becomes more attractive. The typical long-running characteristic of workflows imposes new challenges such as dynamic adaptation of running workflow instances. Windows Workflow Foundation (in short WF) was released by Microsoft as their solution for workflow-driven application development. Although WF contains features that allow dynamic workflow adaptation, the framework lacks an instrumentation framework to make such adaptations more manageable. Therefore, we built an instrumentation framework that provides more flexibility for applying workflow adaptation batches to workflow instances, both at creation time and during an instance's lifecycle. In this paper we present this workflow instrumentation framework and performance implications caused by dynamic workflow adaptation are detailed.

In order to deliver intelligent context-aware services (e.g. notifications of nearby points of interest), location based services are getting a lot of interest. However, deploying these services efficiently is currently hampered by the lack of enabling platforms, especially platforms taking advantage of easy service composition and flexible management. In this paper we detail the design of pCASE, a platform for enabling context aware services. This platform allows easy deployment in both home and business premises and flexible management of complex services taking into account context information such as location and presence. A use case is presented: an intelligent call redirection service which dynamically (re)routes communication sessions depending on the location, occupation and social networks of users.

Notwithstanding IP anycast's introduction in Internet standards dates back to 1993 and its more recent adoption in IPv6 standards, its use in production environments is limited to date. This is mainly because native IP anycast lacks routing scalability and does not support session-based communications, thereby limiting its applicability to single request-response services such as DNS. For this reason, we propose a transparent anycast overlay architecture that retains the strengths of native anycast and neutralizes above-mentioned limitations. The resulting proxy infrastructure unleashes the power of anycast by opening up new opportunities for transparent distributed service provisioning. Taking into account user demands, available resources, network overhead and anycast infrastructure costs, we provide near- optimal heuristics for the placement of proxy nodes and dimensioning the infrastructure in large networks. We show that even modest overlay infrastructures, consisting of a small number of proxy routers, provide an effective stateful anycast solution where the detour via the proxy routers is negligible in terms of extra network load. Furthermore, simulation results illustrate that server state aggregation in the proxy nodes lessens control plane overhead, which contributes significantly to service robustness.

An important current topic is the provisioning of multimedia services to users in fast moving vehicules (e.g., trains or cars). As an example, the focus of the paper is on services to train-users. Different important open challenges will be highlighted. First, an overview of current architectures for network connectivity to trains is presented. An Ethernet-based solution with moving tunnels is detailed. Next, the business case of delivering video services to train users is introduced and critically assessed. A dependable video provisioning service is detailed, together with an overview of the practical deployment of the service.

As the popularity of resource-constrained devices such as hand-held computers increases, a new network service off loading complex processing tasks towards computational resources located in the access- or core network, sounds very promising. In a consumer-oriented environment, characterized by a large diversity in connected devices, a transparent network-based request processing strategy offers a clear flexibility advantage, as the installation and configuration of extra software components on all client devices can be avoided. In this work, this is achieved by linking computational resources to an any cast group, which allows intermediate router nodes to decide upon the target server. It is shown in the paper that the anycast routing problem can be reduced to unicast routing. Consequently, unicast multiple constraints routing algorithms can be applied to compute an optimal path based on several server selection criteria, including server load, path delay, path cost, etc. For this purpose, we envision the SAMCRA algorithm. A new evaluation ordering strategy for previously computed sub-paths is introduced, which guarantees optimality for the complete SAMCRA path between source and destination. Simulation results show that an effective distribution of the job scheduling requests over the available resources can be achieved by applying the described algorithm.

While the Java programming language and the J2EE platform are increasingly popular for implementing business logic on backend platforms, new emerging Java technologies such as JAIN SLEE and SIP Servlet are focusing on the development of low latency Java applications. As J2EE mainly focuses on enterprise applications with complex long lasting transactions, this technology is considered unsuitable for applications with low latency and high throughput characteristics. This paper compares these telecom oriented Java technologies to J2EE both in terms of functionality and through a detailed performance evaluation. JVM performance tuning has been studied as well and is explained in the paper. We performed a SIP proxy benchmark with strict low latency requirements of which the results are presented. Furthermore, design guidelines for J2EE applications are discussed to optimize for low latency behavior together with an interpretation of the obtained performance results.

More and more, content providers offer multimedia services such as Internet TV, multimedia conferencing and online gaming to their customers. These services are characterized by their high sensitivity to network delay and a multicast nature. An overlay network allows for supporting QoS by making reservations in the underlying networks and for multicasting the multimedia streams towards their targets at the overlay layer, without requiring multicast support from the underlying networks. This paper outlines the architecture of a dynamic QoS enabled multicast overlay network and also introduces a set of algorithms to determine an overlay distribution tree that connects a multimedia server to a number of clients. The algorithms construct a tree with a bounded end-to-end delay and minimize the bandwidth that is used. These algorithms are evaluated in terms of bandwidth cost, overlay cost and end-to-end delay. We show that one of our heuristics finds overlay multicast trees that approximate the optimal result in terms of cost and that have a small diameter and a low average delay.

A recent important evolution in broadband access network design is the deployment of IP aware access network elements, which allow to introduce access network services beyond basic triple-play. The focus of this paper is on the management of time-shifted television (tsTV), an IPTV service which allows for watching the broadcast content at real-time or with a (small) time shift. An architecture for a large-scale tsTV service deployment is presented, using co-operating transparent diskless proxy caches in broadband access networks, with an implementation based on the IETF's real-time streaming protocol (RTSP). Caching algorithms have been designed to take into account content popularity and distance metrics. The algorithms make use of the sliding window concept and calculate the optimal trade-off between bandwidth usage efficiency and storage cost. A prototype implementation of a transparent tsTV proxy is presented and evaluated through performance measurements.

A current important trend is the introduction of new services in the access and aggregation network, close to the end user. A major opportunity for such service enabled access networks is providing users with fast and reliable storage, allowing them to transparently access and share their digital media library anytime, anywhere, while guaranteeing data retention. An important issue in deploying such a service is where to put the storage servers, in order to minimize deployment cost without sacrificing performance. This paper presents and evaluates two algorithms for solving the storage server placement problem, minimizing the deployment cost while guaranteeing a low delay for accessing the digital media libraries from any access node. The base algorithm, referred to as SSPA (storage server placement algorithm), assumes the access and aggregation network has unlimited bandwidth. SSPA provides a lower bound on the required number of servers, respecting a maximum delay constraint. The extended algorithm, SSPA, solves the storage server placement problem, respecting both a maximum delay constraint and bandwidth constraints of the network links. It will be shown that the algorithms produce close to optimal results relatively fast

Content distribution networks (CDN) have been increasingly used to deliver bandwidth-intensive multimedia content to a large amount of users. In a CDN, the content is replicated from the origin server to so-called surrogate servers in order to improve the quality of service experienced by the end-users and decrease the network load. However, despite the promising concept, current centralized and distributed CDN architectures lack placement and retrieval algorithms that are both scalable and provide a close to optimal placement. In this article, we propose a novel replica placement algorithm called COCOA (cooperative cost optimization algorithm), suited for a self-organizing hybrid CDN architecture. Our results show that COCOA achieves a performance comparable to the less scalable centralized algorithms, while maintaining the benefits of distributed approaches. Contrary to the more common off-line content replication and management strategies, on-line replication in a self-optimizing CDN puts an additional strain on the network. We explore techniques to control this traffic and study its implications on the performance of the CDN. Because in a CDN content is replicated to geographically distributed surrogate servers, one of the main benefits is its ability to recover from network failures and increase the availability of content during flash crowds. As illustrated in this article, we succeed in making the CDN more robust by effectively reducing the convergence time of the network after the occurrence of such disruptive events

As Massively Multiplayer Online Games enjoy a huge popularity and are played by tens of thousands of players simultaneously, an efficient software architecture is needed to cope with the dynamically changing loads at the server side. In this paper we discuss a novel way to support this kind of application by dividing the virtual world into several parts, called microcells. Every server is assigned a number of microcells and by dynamically redeploying these microcells when the load in a region of the world suddenly increases, the platform is able to adapt to changing load distributions. The software architecture for this system is described and we also provide some evaluation results that indicate the performance of our platform.

Despite the growing popularity and advantages of thin-client systems, they still have some important shortcomings. Current thin-client systems are ideally suited to be used with classic office-applications but as soon as multimedia and 3D gaming applications are used they require a large amount of bandwidth and processing power. Furthermore, most of these applications heavily rely on the Graphical Processing Unit (GPU). Due to the architectural design of thin-client systems, they cannot profit from the GPU resulting in slow performance and bad image quality. In this paper, we propose a thin-client system which addresses these problems: we introduce a realtime desktopstreamer using a videocodec to stream the graphical output of applications after GPU-processing to a thin-client device, capable of decoding a videostream. We compare this approach to a number of popular classic thin-client systems in terms of bandwidth, delay and image quality. The outcome is an architecture for a hybrid protocol, which can dynamically switch between a classic thin-client protocol and realtime desktopstreaming.

Due to natural disasters or intentional attacks, large parts of our telecommunication network can be destroyed. The setup of a wireless shadow network can restore the damaged network connectivity. This paper investigates the need for a network layer solution to integrate such a wireless shadow network with the wired network. It presents the combination of the micromobility protocols MEHROM and mobile IP regional registration as an efficient solution to support terminal mobility. The proposed solution is evaluated in terms of control overhead, required storage and calculation capacity, and functional complexity

To efficiently support the highly dynamic traffic patterns of the current Internet in large-scale switches, we propose a new hybrid optical network design: Overspill Routing In Optical Networks (ORION). By taking advantage of the reduced (electronic) processing requirements of all-optical wavelength switching, the electronic bottleneck is relieved. At the same time, ORION achieves a level of statistical multiplexing comparable to the more traditional point to point WDM solutions, circumventing the bandwidth inefficiencies of all-optical wavelength switched networks, caused by dynamic traffic patterns. The result is a true hybrid optical network design, forming a bridge between these two switching concepts. In this paper the generic concept of ORION is described. An example node design, based on current advanced optical technologies, is described in detail. The ORION concept is also evaluated, comparing it with its two composing technologies, optical wavelength switching and point to point WDM, as well as a third, more trivial, hybrid one, through several case studies

In this paper, we propose the use of a distributed service management architecture for state-of-the-art service-enabled grids. The architecture is capable of performing automated resource and network bandwidth partitioning based on registered grid resource properties and monitored grid service demand. A main characteristic is that it enables the use of different service priority schemes and allows for policy-based differentiation between local and foreign service offerings. Resource and network bandwidth partitioning algorithms are introduced and their performance is evaluated on a sample grid topology using NSGrid, an ns-2 based grid simulator. Our results show that the use of this Service Management Architecture improves resource efficiency, simplifies schedule making decisions, reduces the overall complexity of managing the grid system, and at the same time improves grid service QoS support (with regard to job response times) by automatically making grid resource and network service reservations prior to scheduling.

Design of aggregation networks for delivering multimedia services to fast moving users is an interesting topic. Research has already been devoted to the dimensioning taking into account the movement of the users. However, the effects of packet loss and packet reordering when switching paths have not been taken into account before. This paper presents a design method that aims at minimizing packet loss and packet reordering in the dimensioning and routing process. Consequently, various routing techniques are presented and their performance is thoroughly compared. The results show that for some routing strategies only a slight extra installation cost is required for guaranteeing minimal packet loss and reordering. Deployment of routing schemes in Ethernet networks requires mapping of the calculated routes on different spanning tree instances. A path aggregation algorithm is presented that forms a minimal set of spanning tree instances out of a set of routing paths.

In this paper, the focus is on the management aspects for delivery of multimedia services to fast moving users (e.g., users in trains, cars or airplanes). The considered network architecture consists of two parts: an aggregation network part and an access network part. We designed a management system for the aggregation network. The main functionalities of this management system are: (i) pre-configuration of the QoS (quality of service) tunnels, based on the results of the network capacity planning process, (ii) activation of the QoS tunnels when required and (iii) tunnel reconfiguration when the activated tunnels are insufficient to carry the instantaneous demand or in case of other failures. The main difference with existing management systems is that it is designed to deal with the rapidly moving nature of the traffic conditions and the generation of triggers for tunnel activations or reconfigurations. The management system focuses on multimedia service delivery to trains through high speed Ethernet aggregation networks. An extended GVRP (GARP (generic attribute registration protocol) VLAN registration protocol) and a new GARP protocol (G2RP) were designed and implemented as protocols for the automatic tunnel pre-configuration and activation, respectively. The management system makes use of optimization algorithms for network dimensioning and tunnel path determination. These algorithms are described in detail and their evaluation results are compared for a basic train scenario. Finally, measurement results on the performance of the designed protocols for tunnel pre-configuration and activation, are presented in detail.

Currently, a lot of research has been devoted to (i) content distribution, (ii) traffic engineering, (iii) network monitoring and (iv) service enabling platforms. However, the integration of these four individual concepts in a single platform has not yet been studied in enough detail. In this paper we present an architecture for such a robust content delivery service. The combination of both distributed replication of videos and multi-source traffic engineering tackles specific problems such as congested network parts, overloaded servers and the occurrence of flash crowds. Contrary to most existing systems, the content placement and retrieval algorithms in the presented CDN obtain precise network state information from an integrated monitoring system, allowing even a higher efficiency. To validate the performance of the CDN, an exact placement ILP formulation and various RPA heuristics are implemented and simulated.

With the deployment of broadband Internet access, a new set of applications that is becoming more and more popular are highly interactive services such as online gaming. When the delay experienced in an online game passes a certain threshold, the quality of service (QoS) degrades enormously, as a result the clients are no longer satisfied and might decide to leave the game or to cancel the subscription. Thus, delay is critical to the success of this type of service. Network delay is a result of the way the data is distributed between the players. In this paper we propose the use of a set of game servers. These servers not only play the role of a traditional game server but also form an overlay network which can be used as an application layer routing infrastructure, allowing us to route along low delay paths and thereby increasing the QoS of the game. We study algorithms that determine the ideal location of the servers in a network and look at the performance of the resulting overlay networks in terms of end-to-end delay and the relationship between the number of servers in the overlay network and the offered QoS. We also compare our distributed architecture with both a single server and a peer-to-peer architecture.

With the currently emerging trials for Internet solutions on the train, it is a matter of time before best-effort Internet on the train becomes a reality. However, designing the aggregation network which transports data from fast moving users to service providers, requires extended research because an aggregation network responsible for the reliable transport of broadband multimedia traffic has not been studied before. We propose a flexible recovery strategy for Ethernet networks with dynamic VLANs. It is shown that fast recovery can be achieved in aggregation networks if standard Ethernet protocols are adjusted to co-operate with the fast and efficient link probe mechanism. Additionally a cost-effective design method is presented for networks of realistic size which aims at combining requirements of the commuters (such as end-to-end delay variations) with reliability constraints

A major opportunity for IP network operators to increase the revenue stream generated by their network is a closer and faster integration of services into the IP broadband network. However, with this integration come responsibilities concerning the quality, availability and continuity of these services, which are much harder to fulfil than the requirements for the current IP data traffic. SONAR (service-oriented networking using adaptive resource control) is a network management and operations platform designed to drive a DiffServ/MPLS network so that these responsibilities can be met. In this paper we discuss and evaluate the architecture and algorithms that are the basis for this platform. The architecture decomposes the network management problem in two components: a centralised path computation and a distributed tunnel management. Finally this hybrid on line/off line adaptive traffic engineering approach is evaluated in an modified NS-2 simulator environment.

This paper describes the design of a novel tunnel set-up mechanism for Ethernet access networks. The establishment of VLAN-based tunnels is realised with a "scoped refresh" extension of the GVRP standard. For the distribution of QoS-related reservation parameters, a new GARP-based protocol (G2RP) is developed with a closed loop design for updating the reservation parameters and hop-by-hop admission control. Both protocols are implemented on a Linux test bed using the click modular router. Extensive performance evaluations were performed. The results are compared with Linux implementations of the other tunnel set-up mechanisms.

In case of natural disasters or intentional attacks, telecommunication networks often get heavily damaged and current resilience schemes have proven to be insufficient for rapid recovery of telecommunication services from catastrophic failures. We consider wireless recovery networks as a strong candidate to provide emergency communication resources in case of such severe network outages, and to preserve, with minimal degradation in quality, the distribution of standard communication services. The focus of our work is on the modeling of wireless shadow networks and study their throughput as a function of the system parameters. We address the design problem of determining the required number of antennas and their most appropriate location for a simple topology. For directional antennas we investigate the expected throughput gain as a function of the beam-width. Furthermore, some interesting topics such as the evaluation of multi-hop constellations and modeling the antenna steering strategies, will be briefly introduced.

Active networking is one of the suggested technologies to introduce additional intelligence and programmability in the network and its services. In this paper, the use of active networking to support advanced multicast services providing media transcoding inside the network is investigated. In the multicast service different versions of the streamed data are made available and customers can select a specific version according to their wishes or their capabilities. Based on the active networking facilities of the underlying framework the different versions of the streamed data can be created inside the network, through transformations or transcodings of the original data. Both design and performance issues of the detailed service are discussed. A new multicast tree set-up protocol, taking into account the required transcodings, is introduced. A number of different strategies are discussed optimizing the location of the transcodings as well as the use of bandwidth in the network, while considering the availability of sufficient processing power in the nodes. The performance analysis is done for a voice stream multicast service, addressing the efficiency of the tree set-up strategies, the optimization of network resource utilization, the use of processing power for transcodings, and the resulting quality of streamed voice signals after multiple transcodings.

In this paper, we address the design and implementation of a generic and scalable platform for efficient management of computational resources. The developed platform is called the Intelligent Agent Platform. Its architecture is based on middleware technology in order to ensure easy distribution of the software components between the participating workstations and to exploit advanced software techniques. The computational tasks are referred to as agents, defined as software components that are capable of executing particular algorithms on input data. The platform offers advanced features such as transparent task management, load balancing, run time compilation of agent code and task migration and is therefore denoted by the adjective "Intelligent". The architecture of the platform will be outlined from a computational point of view and each component will be described in detail. Furthermore, some important design issues of the platform are covered and a performance evaluation is presented.

We address the design of a generic and scalable platform for cluster computing. The architecture of the platform is based on middleware technology in order to ensure easy distribution of the software components along the participating workstations and to exploit advanced communication techniques, such as event notification and object registration. The computational tasks are referred to as intelligent agents, which are software agents that are capable of executing particular algorithms on input data. The developed platform offers advanced features such as transparent load balancing, task scheduling, run time compilation of agent code and migration of tasks. The architecture of the platform is outlined from a computational point of view and each component is described in detail. Furthermore, the engineering aspects of the platform are covered. In addition, a sample scenario for computational tasks in the area of telecommunication network design and management is described.

In this paper we address the design of a generic and scalable architecture for connection management and SLA (service level agreement) monitoring of VPNs (virtual private networks). Layer-based design ensures that the architecture is independent of the network technology. Use of advanced software techniques such as run time compilation and intelligent agents allow easy integration of new monitoring and routing algorithms. The architecture offers advanced features such as VPN edge device capability matching, XML-based SLS (service level specification) language, load balancing of computational tasks, and programmable components. The architecture is compliant with the TINA (telecommunication information network architecture) recommendations and its implementation is based on CORBA (common object request broker architecture). In addition, a sample VPN setup and monitoring scenario will be detailed.

In this paper we address the design and implementation aspects of a generic architecture for the NML (network management layer). The focus in this paper will be on SNMP manageable ADSL/ATM networks. SNMP (simple network management protocol) is a standardized protocol which is supported by almost all current computer network devices. The architecture is compliant with the TINA (Telecommunication Information Network Architecture) recommendations and is implemented based on the CORBA (Common Object Request Broker Architecture) standard. During the design and implementation process, a lot of effort has been devoted to the performance, robustness, persistence and consistence of the distributed software under various load and fault scenarios. The most suitable distributed software techniques, like object transactions, concurrency control, event notification and naming service were used and thoroughly tested on their usability. It is also indicated how the core of the described architecture can be extended towards next-generation IP-based networks.

In this paper routing, planning of working capacity, rerouting, and planning of spare capacity in wavelength division multiplexing (WDM) networks are investigated. Integer linear programming (ILP) and simulated annealing (SA) are used as solution techniques. A complex cost model is presented. The spare capacity assignment is optimized with respect to three restoration strategies. The benefit of wavelength conversion, the choice of the fiber line system, and the influence of cost parameter values are discussed, with respect to the different restoration strategies and solution techniques. Wavelength conversion is found to be of limited importance, whereas tunability at the end points of the connections has substantial benefits.