In the realm of Fifth Generation (5 G) and the upcoming Sixth Generation (6 G) networks, the efficient management of network resources becomes increasingly critical, particularly for applications that have strict Quality of Service (QoS) requirements. This paper addresses the complexities associated with Virtual Network Embedding (VNE), a vital process for establishing multiple virtual networks on shared physical infrastructure within the context of network slicing. We introduce the SetpodNet scheduler, a novel orchestration solution that leverages reinforcement learning to enhance the optimization of latency and bandwidth allocation specifically in Kubernetes environments. The SetpodNet scheduler is designed to dynamically adapt to fluctuating slice arrivals and varying resource demands, ensuring that network performance remains consistent and reliable. Through comprehensive experimental evaluations, we demonstrate improvements in slice acceptance ratios and optimizing QoS.

5G and beyond networks aim to support heterogeneous services with strict QoS and isolation requirements. Network slicing addresses these challenges by creating multiple virtual networks over shared infrastructure, each tailored for specific service types. However, despite its potential, the lack of practical, open, and reproducible testbeds for 5G slicing remains a major barrier to experimentation and adoption. In this demo, we present NexSlice, an open-source, Kubernetes-Native testbed that enables the deployment and orchestration of 5G core network slices using open-source components like OpenAirInterface (OAI) and UERANSIM. Our platform supports SST-based slicing, integrates different Radio Access Networks (RANs), enables auto-scaling of user plane functions, and provides real-time monitoring with Prometheus and Grafana. NexSlice aims to bridge the gap between theoretical slicing frameworks and practical, reproducible experimentation, paving the way toward adaptive and automated slice management in future 6G networks.

The Fifth and Sixth Generation (5G/6G) networks aim to support diverse applications with specific QoS and resource needs. Network Slicing has emerged as a key paradigm to meet these demands by creating multiple Virtual Networks (VNs) over shared physical infrastructure. This process, known as Virtual Network Embedding (VNE), maps virtual nodes and links to physical resources. With Kubernetes becoming the dominant orchestration platform, most infrastructures now rely on Kubernetes clusters, which enforce direct pod-to-pod communication, necessitating a direct-link approach to VNE. However, most existing methods focus on path-based link mapping. In this paper, we present DL-ViNE, a Reinforcement Learning(RL)-based algorithm that improves slice acceptance while addressing the specific constraints of Kubernetes-hosted infrastructures.

In 5G networks, physical resources can be virtualized and allocated to separate virtual networks (or network slices), with distinct requirements. The Virtual Network Embedding (VNE) problem consists in finding the optimal mapping of virtual resources (virtual links and nodes) onto a physical infrastructure. A recent trend consists in virtualizing 5G networks using Kubernates (K8s), a popular virtualization technology.In this paper we perform an experimental study to show the limit of using the standard K8s deployment strategy when dealing with dynamically arriving slices in a heavy loaded setting. By deploying the virtual components of a slice one by one, standard K8s is prone to wasting resources and energy due to partially deploying slices that, at the end, are found to be infeasible, due to lack of available resources. We propose an alternative K8s deployment strategy that first solves VNE via a Genetic Algorithm and then, for each slice, deploys either all its components or none. Our experimental results show a notable improvement in slice acceptance, energy efficiency and deployment time. Our work shows that it is necessary to adapt cloud native technologies to the specific requirements of telecommunication scenarios, as they are different from the cloud ones for which such technologies were originally developed.

Thanks to resource virtualization, Physical Network Operators (PNOs) can share their 5G network to multiple Mobile Virtual Network Operators (MVNOs) which can leverage the shared physical infrastructure to deploy their services up to the edge. This allows much more flexibility with respect to the previous generation of cellular networks: MVNO software components can be placed at different locations, can be allocated a certain amount of virtual resources (e.g., bandwidth, CPU cycles), and be reachable via different paths. To the best of our knowledge, strategies to minimize energy consumption while satisfying Service Level Agreements (SLAs) between the PNO and the MVNOs are still largely missing, particularly if it is required to take the nonlinearity of delays into account. To fill this gap, we formulate the problem of joint placement of software components, routing of user requests and resource dimensioning. SLAs are represented in terms of latency and reliability constraints. Via Column Generation, we obtain exact solutions in real-sized networks. Our numerical results show that we can save up to 50% energy in networks with up to 30 nodes compared to the state-of-the-art algorithms, which are focused on placement or resource minimization.

Network slicing in the upcoming Telecom generation is a fundamental feature which is deployed to satisfy the various demands in term of data rate and latency. On the other hand, it is seen as a topic that imposes other questions such as the coexistence of physical and virtual functions. In this context, we consider the resource management problem for 5G networks slicing since the solution searches to optimally allocate multiple Virtual Network Requests (VNRs) on a substrate virtualized physical network. In this demo, we present an integrated framework that uses an agile service platform (Kube5G) to deploy one of the VNE proposed solutions with zero-touch configuration. The aim of this integration is to validate the proposed solution and to practically study the performance differences among multiple algorithms that will be conducted later as well. The overview of the process is shown in steps as exposing the resources’ availability of the Physical Nodes (PN), which will be the input of the orchestration algorithm. Successively, the last takes the suitable decision to deploy VNRs on a substrate network, based on the VNRs’ demands such as CPU and radio resources and PNs’ availability. Afterwards, the decision will be sent to the platform to host the virtual nodes on the chosen physical machines. Bearing in mind that the essential objective of this algorithm is to achieve a better resource usage and increase the VNR acceptance ratio on the physical nodes with respect to the constraints that might affect the performance.

The 5G telecommunication ecosystem is expected to dynamically support new and various applications from the industrial and the service sectors that are very heterogeneous in terms of QoS and resources’ requirements. In this context, a promising important concept for network resource management is emerging, denoted by Network Slicing. It involves decisions on embedding and managing several virtual networks on the same physical resources. This problem in its simplified form can be modeled by the Virtual Network Embedding (VNE) problem. In this paper, we propose a new resolution method, in which we first reduce the set of admitted routes and then solve an integer program. Our proposed approach is then compared to the optimal solution and to a method from the state of the art. Obtained results show that our approach provides good result in terms of slice acceptance ratio and resource consumption while reducing the overall complexity and runtime.

In this paper, we consider the Virtual Network Embedding (VNE) problem for 5G networks slicing. This consists in optimally allocating multiple Virtual Networks (VN) on a substrate virtualized physical network while maximizing among others, resource utilization, maximum number of placed VNs and network operator's benefit. We solve the online version of the problem where slices arrive over time. We propose the use of the Nested Rollout Policy Adaptation (NRPA) algorithm, a variant of the well known Monte Carlo Tree Search (MCTS). Both algorithms learn by randomly simulating the embedding, but NRPA also learns how to perform better simulations over time. Performance analysis with different scenarios, show that NRPA improves acceptance and reward ratios (by up to 69% and 65%). We also show how a smart initialization of the learning process can help improve the results furthermore (up to a 12.5% increase of acceptance ratio).

Summary This paper presents an efficient algorithm with performance guarantee to solve task scheduling problem on hybrid platforms with energy constraint and communication delays. The underlying platform architecture in this work is composed of two types of resources, CPU and GPU, often called hybrid parallel multicore platforms. We focus on finding a generic approach to schedule applications presented by Directed Acyclic Graph (DAG), which minimizes the makespan by considering communication delays and respecting an energy constraint. A two‐phase algorithm is proposed with a performance guarantee of 6 compared with the optimal solution; the first phase consists in solving the assignment problem to find the type of processor assigned to execute the tasks (CPU or GPU) using a linear program. In the second phase, we calculate the start execution time of each task to generate a feasible schedule. Finally, we test our algorithm on a large number of instances. These tests demonstrate that the proposed algorithm achieves a close‐to‐optimal performance.

Summary Many of today's important applications of our everyday lives, eg, weather forecast, design of plane and car shapes, medical analysis, or search engine queries depend on massively parallel computer programs executed in data centers. A large amount of energy is used to power them, and it is of primary importance to compute more efficiently to sustain the increasing demand of computing power while keeping energy consumption reasonable. One promising research path in this domain is heterogeneous systems since specific computing resources (processors, accelerators, etc) are more adapted to efficiently execute parts of applications. Nevertheless, the exploitation of these platforms raises new challenges in terms of application management optimization. The aim of our work is to determine effective algorithms to exploit these heterogeneous platforms by finding appropriate application mapping and scheduling to optimize the execution time and energy consumption with respect to various constraints. To achieve this goal, there is a need of a detailed modeling of the applications and the underlying hardware to be able to find realistic solutions. In this paper, we propose such a model, provide two implementations with state‐of‐the‐art tools, and propose a fast greedy online resolution algorithm and preliminary mapping and scheduling numerical results.