The increasing demand for compute intensive Internet of Thing (IoT) applications has accelerated the adoption of multi-access edge Computing (MEC) to offload tasks from resource constrained devices to edge servers. However, making optimal offloading decisions in multi-user MEC environments is challenging due to the dependencies between tasks, resource constraints, and the need to preserve user privacy. In this work, we propose FEDORA, a federated ensemble reinforcement learning framework for directed acyclic graph (DAG)-based task Offloading and resource allocation in MEC environments, that integrates twin delayed deep deterministic policy gradient (TD3) for continuous resource allocation and multi-head deep Q-networks (DQN) for discrete offloading decisions. To handle task dependencies, we model applications as DAGs and generate feature embeddings for offloading decisions. Our federated learning (FL) approach uses local training at MEC level and periodic model aggregation at a global server to preserve data privacy. Finally, extensive simulations across different DAG topologies demonstrate that FEDORA reduces system costs and improves task completion rates compared to state-of-the-art baselines including FL-DQN, FL-DDPG, FedAvg, FedNova, and SCAFFOLD, highlighting its scalability and robustness in large scale MEC deployments.

In this paper we address the challenge of efficiently deploying Virtual Network Functions (VNFs) in network infrastructures. This is particularly crucial when facing resource fragmentation, where available resources are not fully utilized due to the fluctuating allocation and deallocation of virtual network requests. Traditional optimization techniques often fall short in managing the dynamic complexities of VNF placement. To overcome this, we introduce a novel online VNF placement strategy using Deep Reinforcement Learning (DRL) combined with a Reward Constrained Policy Optimization (RCPO). This method leverages the flexibility of DRL and the constraint integration capacity of RCPO, ensuring compliance with performance and resource limitations while minimizing resource fragmentation. The results demonstrate that our DRL-based method surpasses existing methods, resulting in more effective resource management and less resource fragmentation.

The recent networking trends driven primarily by the different virtualization technologies, such as Network Function Virtualization (NFV) and Service Function Chaining (SFC) pave the way for next-generation network services. In the 5G and beyond era, such services usually have strict delay requirements and the wider adoption of the distribution of their computational needs across the Edge-to-Cloud continuum is certainly a step in the right direction. However, the majority of the optimization solutions for placing the virtualized services so far focus on server selection, leaving other areas such as the impact of Non-Uniform Memory Access (NUMA) and CPU core selection underexplored. In this work, we herein formulate the problem of placing services as SFCs on an Edge/Cloud infrastructure, as a Mixed Integer Programming (MIP) problem. Then, we propose a heuristic algorithm called “Dynamic numa node Selection through Cores consolidation – DySCo" to solve it, which optimizes the placement in terms of server, NUMA and core selection. To the best of our knowledge, this is the first attempt to optimize network service placement in an Edge-Cloud interplay. Extensive simulation evaluation shows that DySCo is able to perform close to optimal while finding a solution in a real time fashion. Compared to a mix of baselines and modified solutions from the literature to treat this new problem, DySCo reduces on average the deployment cost by 17.53% and the delay by 28.88% for a given SFC.

Low-earth-orbit satellite networks (LSNs) offer an enhanced global connectivity and a wide range of applications such as disaster response and military operations, among others. Each specific application can be represented by a service function chain (SFC) in which each function is considered as a task in the application. Our objective is to optimize the long-term system performance by minimizing the average end-to-end delay of SFC deployments in LSNs. To achieve this, we formulate a dynamic programming (DP) problem to derive an optimal placement policy. To overcome the computational intractability, the need for statistical knowledge of SFC requests, and centralized decision-making challenges, we present a multi-agent Q-learning approach where satellites act as independent agents. To facilitate performance convergence in non-stationary agents' environments, we let agents to collaborate by sharing designated learning parameters. In addition, agents update their Q-tables via two distinct rules depending on selected actions. Extensive experimentation shows that our approach achieves convergence and performance relatively close to the optimum obtained by solving the formulated DP equation.

In the 5G/6G era of networking, computational offloading, i.e., the act of transferring resource-intensive computational tasks to separate external devices in the network proximity, constitutes a paradigm shift for mobile task execution on Edge Computing infrastructures. However, in order to provide firm Quality of Service (QoS) assurances for all the involved users, meticulous planning of the offloading decisions should be made, which potentially involves inter-site task transferring. In this paper, we consider a multi-user, multi-site Multi-Access Edge Computing (MEC) infrastructure, where mobile devices (MDs) can offload their tasks to the available edge sites (ESs). Our goal is to minimize end-to-end delay and energy consumption, which constitute the sum cost of the considered system, and comply with the MDs’ application requirements. To this end, we introduce a two-stage Reinforcement Learning (RL)-based mechanism, where the MDs-to-ES task offloading and the ES-to-ES task transferring decisions are iteratively optimized. The proper operation, effectiveness and efficiency of our proposed offloading mechanism is assessed under various evaluation scenarios.

Recent trends in Network Function Virtualization (NFV) combined with Internet of Things (IoT) and 5G applications have reshaped the network service offering. In particular, Service Function Chains (SFCs) can associate network functions with physical and virtual resources towards providing a complete network service. Concurrently, the management of a continuously expanding network and the fulfillment of the applications’ requirements pave the way for autonomic network solutions. Intent Based Networking (IBN) is a novel paradigm that aims to achieve the automatic orchestration of network services and the assurance of their performance. Accordingly, in this paper, we propose a novel automated network assurance model, based on Model Predictive Control, to guarantee the Quality of Service (QoS) and security requirements of multi-tenant and IBN-enabled SFCs. In this context, corrective decisions are proactively taken, in the form of incoming intent relocations among the SFCs. The results reveal that our model can assure with high probability the application requirements and minimize QoS violations.

Modern networking paradigms like Service Function Chaining (SFC) allow for services to be broken down to a series of ordered and interconnected Virtualized Network Functions (VNFs) that can be hosted in generic servers in EdgeCloud datacenters. Nonetheless, a critical issue arises, when a hardware or software failure occurs and the VNFs of an SFC need to be repositioned, allowing to autonomously bring the system back to its normal operation, a process called self-healing. In this paper, a distributed methodology is proposed that aims to address this challenge, considering the requirements of all involved actors. Specifically, a Reinforcement Learning (RL) based algorithm is proposed that allows to iteratively optimize and determine an SFC healing solution upon a datacenter failure. As a second stage, a revenue-driven resource allocation mechanism is integrated, to resolve the contention for resources in an already functional datacenter that potentially occurs due to the repositioning. Various simulation scenarios prove the efficiency of our proposed resilient healing mechanism.

The evolution of the Industrial Internet of Things (IIoT) and edge computing enables resource-constrained mobile robots to offload the computationally intensive localization algorithms. Naturally, utilizing the remote resources of an edge server to offload these tasks encounters the challenge of a joint co-design in communication, control, estimation, and computing infrastructure. We introduce a set-based estimation offloading framework, for the specific case of the navigation of a unicycle robot toward a target position. The robot is subject to modeling and measurement uncertainties, and the estimation set is calculated using overapproximation techniques that alleviate additional computations. A switching set-based control mechanism provides accurate navigation and triggers more precise estimation algorithms when needed. To guarantee the convergence of the system and optimize the utilization of remote resources, a utility-based offloading mechanism is designed, which takes into account both the dynamic network conditions and the available computing resources at the network edge. The performance of the proposed framework is demonstrated through simulations and comparison with alternative offloading schemes.

Although mobile devices today have powerful hardware and networking capabilities, they fall short when it comes to executing compute-intensive applications. Computation offloading (i.e., delegating resource-consuming tasks to servers located at the edge of the network) contributes toward moving to a mobile cloud computing paradigm. In this work, a two-level resource allocation and admission control mechanism for a cluster of edge servers offers an alternative choice to mobile users for executing their tasks. At the lower level, the behavior of edge servers is modeled by a set of linear systems, and linear controllers are designed to meet the system’s constraints and quality of service metrics, whereas at the upper level, an optimizer tackles the problems of load balancing and application placement toward the maximization of the number the offloaded requests. The evaluation illustrates the effectiveness of the proposed offloading mechanism regarding the performance indicators, such as application average response time, and the optimal utilization of the computational resources of edge servers.

Unmanned Aerial Vehicles (UAV) facilitate the development of Internet of Things (IoT) ecosystems for smart city and smart environment applications. This paper proposes the adoption of Edge and Fog computing principles to the UAV based forest fire detection application domain through a hierarchical architecture. This three-layer ecosystem combines the powerful resources of cloud computing, the rich resources of fog computing and the sensing capabilities of the UAVs. These layers efficiently cooperate to address the key challenges imposed by the early forest fire detection use case. Initial experimental evaluations measuring crucial performance metrics indicate that critical resources, such as CPU/RAM, battery life and network resources, can be efficiently managed and dynamically allocated by the proposed approach.