Toward Using Reinforcement Learning for Trigger Selection in Network Slice Mobility

• Published in: IEEE journal on selected areas in communications 2021-07, Vol.39 (7), p.2241-2253
• Main Authors: Addad, Rami Akrem, Dutra, Diego Leonel Cadette, Taleb, Tarik, Flinck, Hannu
• Format: Article
• Language: English
• Subjects: 5G mobile communication; agent-based resource orchestration; Algorithms; Cloud computing; Computer architecture; deep reinforcement learning; Edge computing; Heuristic algorithms; Machine learning; multi-access edge computing; Network function virtualization; Network latency; Network slicing; network softwarisation; Optimization; Reinforcement learning; Resource allocation; Service level agreements
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2021.3078501

Recent 5G trials have demonstrated the usefulness of the Network Slicing concept that delivers customizable services to new and under-serviced industry sectors. However, user mobility's impact on the optimal resource allocation within and between slices deserves more attention. Slices and their dedicated resources should be offered where the services are to be consumed to minimize network latency and associated overheads and costs. Different mobility patterns lead to different resource re-allocation triggers, leading eventually to slice mobility when enough resources are to be migrated. The selection of the proper triggers for resource re-allocation and related slice mobility patterns is challenging due to triggers' multiplicity and overlapping nature. In this paper, we investigate the applicability of two Deep Reinforcement Learning based algorithms for allowing a fine-grained selection of mobility triggers that may instantiate slice and resource mobility actions. While the first proposed algorithm relies on a value-based learning method, the second one exploits a hybrid approach to optimize the action selection process. We present an enhanced ETSI Network Function Virtualization edge computing architecture that incorporates the studied mechanisms to implement service and slice migration. We evaluate the proposed methods' efficiency in a simulated environment and compare their performance in terms of training stability, learning time, and scalability. Finally, we identify and quantify the applicability aspects of the respective approaches.