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.

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.

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.

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.

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.

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.

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 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.

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.

Point Cloud (PC) streaming has recently attracted research attention as it has the potential to provide six degrees of freedom (6DoF), which is essential for truly immersive media. PCs require high-bandwidth connections, and adaptive streaming is a promising solution to cope with fluctuating bandwidth conditions. Thus, understanding the impact of different factors in adaptive streaming on the Quality of Experience (QoE) becomes fundamental. Mixed Reality (MR) is a novel technology and has recently become popular. However, quality evaluations of PCs in MR environments are still limited to static images. In this paper, we perform a subjective study on four impact factors on the QoE of PC video sequences in MR conditions, including quality switches, viewing distance, and content characteristics. The experimental results show that these factors significantly impact QoE. The QoE decreases if the sequence switches to lower quality and/or is viewed at a shorter distance, and vice versa. Additionally, the end user might not distinguish the quality differences between two quality levels at a specific viewing distance. Regarding content characteristics, objects with lower contrast seem to provide better quality scores.

3D objects are important components in Mixed Reality (MR) environments as they allow users to inspect and interact with them in a six degrees of freedom (6DoF) system. Point clouds (PCs) and meshes are two common 3D object representations that can be compressed to reduce the delivered data at the cost of quality degradation. In addition, as the end users can move around in 6DoF applications, the viewing distance can vary. Quality assessment is necessary to evaluate the impact of the compressed representation and viewing distance on the Quality of Experience (QoE) of end users. This paper presents a demonstrator for subjective quality assessment of dynamic PC and mesh objects under different conditions in MR environments. Our platform allows conducting subjective tests to evaluate various QoE influence factors, including encoding parameters, quality switching, viewing distance, and content characteristics, with configurable settings for these factors.

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%.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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%.

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.

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.

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.

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.

Future processing, storage and communication services will be highly pervasive: people, smart objects, machines and the surrounding space (all embedding devices such as with sensors, RFID tags etc.) will define a highly decentralized cyber environment of resources interconnected by dynamic networks of networks. As communications will extend to cover any combination of ’people, machines and things’, future networks will be increasingly complex and heterogeneous, yet always endorsed with the challenging task of ensuring end-to-end QoS. This paper proposes the groundwork for an advanced cognitive networking paradigm exploitable in future wired and wireless infrastructures: a decentralized cognitive plane to allow for cross-layer, cross-node and cross-network domain self-management, self-control and self-optimization, while being compatible with legacy management and control systems.

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.

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.

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.

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

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.