5G and edge: A reinforcement learning approach for Virtual Network Embedding with cost optimization and improved acceptance rate

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Vol. 247; p. 110434
• Main Authors: Moreira, Cristiano L., Kamienski, Carlos A., Bianchi, Reinaldo A.C.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-06-2024
• Subjects: 5G network slicing; Artificial Intelligence; Cloud computing; Edge computing; Network virtualization; Reinforcement Learning; Virtual Network Embedding; Cloud computing; Network virtualization; 68T05; Virtual Network Embedding; Artificial Intelligence; Reinforcement Learning; 68M10; 5G network slicing; Edge computing
• ISSN: 1389-1286; 1872-7069
• DOI: 10.1016/j.comnet.2024.110434

5G technologies are fueling a revolution across numerous industries, including manufacturing, healthcare, and entertainment, by enabling the development and deployment of novel applications at the network’s edge. To meet the demanding service level agreements of these industries, a dynamic and adaptable infrastructure strategy that combines cloud and edge computing models is needed. This hybrid approach offers the benefits of both centralized cloud processing and decentralized edge computing, optimized for responsiveness and efficiency. A key element for success is an orchestration mechanism that dynamically allocates resources to ensure the infrastructure can adapt to fluctuating demands in real time, optimizing resource utilization and meeting SLA requirements. Among these mechanisms, virtual network embedding (VNE) and dynamic resource management (DRM) have emerged as tools for defining where and how edge technology should be used. However, current VNE approaches struggle to adapt to real-time fluctuations in demand across geographically distributed edge resources. This work introduces a novel resource allocation algorithm, the VNE-CRS, which uses an Artificial Intelligence technique called Reinforcement Learning to orchestrate resources across multiple domains. This approach benefits from the strength of Reinforcement Learning: its ability to consider the entire problem from beginning to end while incorporating various aspects of 5G Quality of Service Indicators for optimal decision-making. Experiments were conducted to evaluate the performance of VNE-CRS against state-of-the-art algorithms for multi-domain edge environments. Results have shown that employing Reinforcement Learning techniques for VNE resource allocation yields performance gains of 12.32 percentage points in comparison with the GRC algorithm and 28.80 percentage points in comparison with the base edge environment, presenting an acceptability rate closer to the Public Cloud environment with all benefits of edge environment. In conclusion, VNE-CRS offers an efficient solution for resource allocation in 5G environments, achieving superior performance and transforming the VNE architecture into a comprehensive orchestration system that optimizes infrastructure utilization for strategic long-term benefits.
•This paper introduces an algorithm to address the Virtual Network Embedding Problem.•The proposed algorithm uses Reinforcement Learning, a form of Artificial Intelligence.•It aims to optimize end-to-end infrastructure by incorporating edge requirements.•The algorithm functions as an orchestrator.•It is agnostic to Infrastructure Domains.