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.

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

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

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.

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

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

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

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

The advent of 6G networks promises unprecedented advancements in communication technologies, demanding innovative solutions for service placement across the Compute Continuum (CC), where computing resources are distributed across the network area, from edge to cloud. This paper explores a novel approach for service placement in 6G networks using Reinforcement Learning (RL) techniques. By leveraging the dynamic decision-making capabilities of RL, this work addresses the complexities of distributing services across heterogeneous computing resources to enhance network performance, reduce latency, and improve resource utilization. An extensive evaluation, based on a real dataset collected from commercial 4G/5G networks, was conducted under various network conditions and workloads to evaluate the effectiveness of the proposed approach. Results highlight the adaptability of our RL-driven model to dynamic network environments, demonstrated by its capacity to optimize for multiple objectives simultaneously. Our analysis also reveals that while single-objective heuristics can outperform RL in specific, limited scenarios, these struggle to handle increasing complexity. These findings highlight the viability of RL as a powerful tool for intelligent service management in next-generation communication systems, paving the way for more resilient and efficient 6G network architectures.

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.

As Virtual Reality (VR) and Extended Reality (XR) technologies continue to evolve, exploring the deployment of these applications on distributed computing resources across the cloud continuum emerges as a promising research direction for improving immersive experiences. This paper reviews the current state-of-the-art on cloud-native VR applications, explaining the synergies between modern cloud infrastructures and recent advances in VR technology. In addition, the paper discusses an exhaustive list of existing literature to identify essential methodologies, architectures, and frameworks for the proper deployment of cloud-native VR applications. Furthermore, the paper analyzes the challenges associated with leveraging cloud infrastructures for VR and XR applications concerning its stringent requirements, including scalability, latency, and bandwidth. The paper highlights recent efforts on resource allocation and networking, aiming to meet the requirements of these low-latency applications in the cloud continuum. Lastly, the paper presents open challenges and research directions in the field, such as optimal containerization of VR components and efficient networking solutions towards reliable VR experiences. Addressing these challenges will lead to the widespread adoption of cloud-native VR applications, ultimately enabling more immersive, accessible, and scalable virtual experiences.

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

The rapid evolution of next-generation mobile networks introduces challenges and opportunities in achieving various use cases with extremely low latency, high data rates, and dense user connectivity. However, these objectives can lead to the higher energy consumption of mobile networks, particularly within the Radio Access Network (RAN), which generally consumes 75% of the mobile networks total energy consumption. The current studies available focus mainly on the energy consumption of the next-generation Core Network (5GC), while this demonstration provides a better understanding of the energy consumption of the RAN components. The demonstration provides energy observability leveraging various open-source software tools to measure and monitor RAN energy consumption trends deployed on the Kubernetes platform — a widely adopted container orchestration platform. Moreover, this demonstration shows energy consumption on different RAN architectures — Monolithic, Disaggregated, and Control Plane and User Plane Separation (CUPS) — utilizing tools such as Kepler and Scaphandre for comprehensive energy measurement.

Recently, container-based solutions have become de facto compute units of modern cloud-native applications. However, the exponential growth in data traffic and the power consumption of these technologies to handle high data traffic alarm the strong need for energy evaluation approaches in containerized clouds. Furthermore, the proliferation of highly distributed edge clouds raises additional concerns regarding the power consumption of future cloud architectures. This article presents a detailed overview of methods and techniques for monitoring power consumption within popular cloud platforms. The study offers an in-depth evaluation of these approaches, demonstrating variations in measured power consumption based on the chosen technique. A well-known container orchestration platform named Kubernetes (K8s) has been applied in our extensive measurements. This work argues that energy-efficient container clouds will play a vital role in building a more sustainable and eco-friendly digital infrastructure by optimizing power consumption and reducing carbon footprint, paving the way for a greener future. The paper also discusses open challenges and future research directions on energy sustainability, leading to the conclusion, offering lessons learned and prospects on potential solutions to foster sustainable practices within the container ecosystem.

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.

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.

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%.

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.

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.

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.

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.

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.