Toward Using Reinforcement Learning for Trigger Selection in Network Slice Mobility

Rami Akrem Addad, Diego Leonel Cadette Dutra, Tarik Taleb, Hannu Flinck

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

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.

Original languageEnglish
Number of pages13
JournalIEEE Journal on Selected Areas in Communications
DOIs
Publication statusE-pub ahead of print - May 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • 5G
  • 5G mobile communication
  • Agent-based Resource Orchestration
  • Cloud computing
  • Computer architecture
  • Deep Reinforcement Learning
  • Heuristic algorithms
  • Multi-access Edge Computing
  • Network function virtualization
  • Network Slicing
  • Network Softwarisation
  • Reinforcement learning
  • Service level agreements

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