R-Learning-based admission control for service federation in multi-domain 5G networks

Proceedings of: IEEE Global Communications Conference (GLOBECOM), 7-11 Dec. 2021, Madrid, Spain.Network service federation in 5G/B5G networks enables service providers to extend service offering by collaborating with peering providers. Realizing this vision requires interoperability among providers towards end-to-end service orchestration across multiple administrative domains. Smart admission control is fundamental to make such extended offering profitable. Without prior knowledge of service requests, the admission controller (AC) either determines the domain to deploy each demand or rejects it to maximize the long-term average profit. In this paper, we first obtain the optimal AC policy by formulating the problem as a Markov decision process, which is solved through the policy iteration method. This provides the theoretical performance bound under the assumption of known arrival and departure rates of demands. Then, for practical solutions to be deployed in real systems, where the rates are not known, we apply the Q-Learning and R-Learning algorithms to approximate the optimal policy. The extensive simulation results show that learning approaches outperform the greedy policy and are capable of getting close to optimal performance. More specifically, R-learning always outperformed the rest of practical solutions and achieved an optimality gap of 3-5% independent of the system configuration, while Q-Learning showed lower performance and depended on discount factor tuning.This work has been partially funded by the MINECO grant TEC2017-88373-R (5G-REFINE), the EC H2020 5Growth Project (grant no. 856709), and Generalitat de Catalunya grant 2017 SGR 1195

Vertical-oriented 5G platform-as-a-service: user-generated content case study

5G realizes an impactful convergence, where Network Functions Virtualization (NFV) and cloud-native models become fundamental for profiting from the unprecedented capacity offered at the 5G Radio Access Network (RAN). For providing scalability and automation management over resources in 5G infrastructure, cloud-native and Platform as a service (PaaS) are proposed as solutions for paving the way for vertical applications in 5G. This paper leverages cloud-native models, PaaS, and virtual testbed instances to provide key platform provisioning and service life-cycle management features to a selected User Generated Content (UGC) scenario in multimedia applications. Specifically, this article and results show how service-level telemetry from a UGC cloud-native application is used to automatically scale system resources across the NFV infrastructure.Comment: Previous version of the paper is accepted in IEEE Future Networks World Forum (FNWF), Montreal, 202

ML-driven provisioning and management of vertical services in automated cellular networks

One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a large-scale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.This work was supported by the EU Commission through the 5GROWTH project (Grant Agreement No. 856709), Spanish MINECO 5G-REFINE project (TEC2017-88373-R), and Generalitat de Catalunya 2017 SGR 1195.Publicad

A Maximum Fair Bandwidth Approach for Channel Assignment

Abstract-Multi-Channel Multi-Radio WMNs are promising solutions for overcoming the limited capacity problem in multihop wireless networks. In these WMNs, each mesh router is equipped with multiple radios and each radio operates in a distinct frequency band. Channel assignment is the key issue that should be addressed in these networks. In this paper we propose a channel assignment scheme with the objective of maximizing per-flow bandwidth with fairness consideration to equalize the bandwidth assignment of flows. A novel problem formulation as multi-objective non-linear optimization problem is developed. We propose a heuristic randomized channel assignment algorithm, MFPFB, to obtain an approximate solution. The MFPFB assigns channels based on the interference level experienced by each flow, which is derived from the given traffic pattern and the proposed interference model, DWIG. For a given channel assignment, a simple algorithm allocates bandwidth for each flow. We used numerical and ns-2 simulations to compare our algorithm against others, investigate effect of routing mechanisms and to validate our model. The result indicates an improvement of up to 20% in the effectiveness of bandwidth assignment

Multi-objective embedding of software-defined virtual networks

Softwarization is the current trend of networking based on the success of technologies like Software Defined Networking (SDN) and Network Virtualization. Network as a Service (NaaS) is a new paradigm based on virtualization that enables customers to instantiate their virtual networks over a physical substrate network, mapping necessary resources by a Virtual Network Embedding (VNE) algorithm. Each VNE algorithm defines a resource allocation strategy of the NaaS provider, and determines its expenditures and revenues. Even though the problem of VNE has been widely investigated in recent years, virtualization in SDN introduces new challenges due to the new role of the controller and additional architectural constraints. In this paper, we investigate the VNE problem where both virtual and substrate networks are software defined. We propose a mathematical programming formulation that considers both the objectives of the NaaS provider (profit maximization) and the customers (switch-controller delay minimization). Proposing new design metrics (i.e., k-hop delay, correlation, and distance), we develop a heuristic algorithm, and prove its effectiveness through extensive simulations in the well-known VNE evaluation tool, ALEVIN, and comparisons with other algorithms and mathematical bounds

Prioritized deployment of dynamic service function chains

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

ML-driven Provisioning and Management of Vertical Services in Automated Cellular Networks

One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a large-scale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.This work was supported by the EU Commission through the 5GROWTH project (Grant Agreement No. 856709), Spanish MINECO 5G-REFINE project (TEC2017-88373-R), and Generalitat de Catalunya 2017 SGR 1195. © 2022, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

6G Technology Overview (third edition)

6G aims to address diverse, often competing needs, such as vastly increased data rates, a massive scale of communicating devices, energy efficiency, and the simultaneous demand for both high data rates and low communication latency. The White Paper covers several groundbreaking technologies pivotal for 6G evolution, such as terahertz frequencies, 6G radio access, integrated sensing and communication, non-terrestrial networks and more. For each technology, the White Paper offers background information, explains its relevance to 6G, presents key problems, and provides a thorough review of the current state of the art. The paper emphasises that while the listed technologies form the foundation for 6G, the landscape will continuously evolve. Subsequent versions of the white paper will spotlight new technologies and integrate them into a cohesive system. The “6G Technology Overview” concludes that 6G, with its vast potential, aims not only to meet the diverse requirements of novel use cases but also to ensure sustainability, user-friendliness, and ease of service deployment