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Latency-aware content caching and cost-aware migration in SDN based on MEC

Abstract

In the mobile edge computing environment, edge servers, edge base stations and other intermediate servers have limited computing, storage resources and limited coverage, resulting in increased content access delays and reduced user experience quality. Content caching methods based on file value and corresponding algorithms are proposed. This method uses Markov chains to predict the mobile user's trajectory in the next time slot, and active caching and cache replacement algorithms are designed to minimize content access latency. In addition, because edge servers are connected to many different access points or base stations, whether or not to migrate the ongoing edge services and where to migrate the services are the key issues to consider when any user moves outside the service area of the related edge servers. To reduce the overhead of service migration due to the mobility of users and the complex topological relationship between edge servers, a de-centralized service migration algorithm is proposed, which integrates the analysis and selection of the optimal edge base station and service placement decisions with the objective of minimizing the overhead of service migration. The experimental results show that the proposed edge caching and service migration methods reduce content access latency and service migration overhead.

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Acknowledgements

The work was supported by the National Natural Science Foundation (NSF) under Grants (Nos. 62171330, 61873341, 61771354), Key Research and Development Plan of Hubei Province (No. 2020BAB102), Open Foundation of Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources (Ministry of Education) (No. TDSYS202105). Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.

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Correspondence to Chunlin Li.

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Li, C., Zhu, L. & Luo, Y. Latency-aware content caching and cost-aware migration in SDN based on MEC. Wireless Netw 27, 5329–5349 (2021). https://doi.org/10.1007/s11276-021-02813-w

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Keywords

  • Deep Q learning
  • Mobile edge computing
  • SDN
  • Edge caching
  • Service migration