Q-learning based collaborative cache allocation in mobile edge computing

Wei Che Chien, Hung Yen Weng, Chin-Feng Lai

Research output: Contribution to journalArticle

Abstract

The rapid development of Augmented Reality (AR), Virtual Reality(VR), Internet of Things (IoT), and high-definition video has caught the attention of low latency and high bandwidth network requirements. For huge data transmission, cache technology has been regarded as an effective solution for reducing transmission time from users to remote clouds. However, the increasing variety of cached data has caused the challenge of cache performance. Based on high flexibility, scalability, and deployability of the characteristics of Software-Defined Networking (SDN), this study proposes a collaborative cache mechanism in multiple Remote Radio Heads (RRHs) to multiple Baseband Units (BBUs). In addition, the traditional rule-based and metaheuristics methods are difficult to consider all environmental factors. To reduce the traffic load of backhaul and transmission latency from the remote cloud, we use Q-learning to design the cache mechanism and propose an action selection strategy for the cache problem. Through reinforcement learning to find the appropriate cache state. The simulation results show that the proposed method can effectively improve the cache performance.

Original languageEnglish
Pages (from-to)603-610
Number of pages8
JournalFuture Generation Computer Systems
Volume102
DOIs
Publication statusPublished - 2020 Jan 1

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Augmented reality
Reinforcement learning
Data communication systems
Virtual reality
Scalability
Bandwidth
Internet of things
Software defined networking

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

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abstract = "The rapid development of Augmented Reality (AR), Virtual Reality(VR), Internet of Things (IoT), and high-definition video has caught the attention of low latency and high bandwidth network requirements. For huge data transmission, cache technology has been regarded as an effective solution for reducing transmission time from users to remote clouds. However, the increasing variety of cached data has caused the challenge of cache performance. Based on high flexibility, scalability, and deployability of the characteristics of Software-Defined Networking (SDN), this study proposes a collaborative cache mechanism in multiple Remote Radio Heads (RRHs) to multiple Baseband Units (BBUs). In addition, the traditional rule-based and metaheuristics methods are difficult to consider all environmental factors. To reduce the traffic load of backhaul and transmission latency from the remote cloud, we use Q-learning to design the cache mechanism and propose an action selection strategy for the cache problem. Through reinforcement learning to find the appropriate cache state. The simulation results show that the proposed method can effectively improve the cache performance.",
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Q-learning based collaborative cache allocation in mobile edge computing. / Chien, Wei Che; Weng, Hung Yen; Lai, Chin-Feng.

In: Future Generation Computer Systems, Vol. 102, 01.01.2020, p. 603-610.

Research output: Contribution to journalArticle

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