Service Caching Strategy based on Edge Computing and Reinforcement Learning

With the rapid development of the Internet of Things in recent years, there has been a dramatic increase in terminal units and new computationally and data-demanding applications. A terminal unit uploads data to the cloud server, which will be transmitted back to the terminal unit after certain oper...

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Bibliographic Details
Published in:International journal of performability engineering 2022-05, Vol.18 (5), p.350
Main Authors: Chengjie, Xu, Dongcheng, Li, W. Eric, Wong, Man, Zhao
Format: Article
Language:English
Subjects:
Algorithms
Cache
Caching
Cloud computing
Comparative studies
Edge computing
Impact analysis
Internet of Things
Mobile computing
Network latency
Servers
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Summary:With the rapid development of the Internet of Things in recent years, there has been a dramatic increase in terminal units and new computationally and data-demanding applications. A terminal unit uploads data to the cloud server, which will be transmitted back to the terminal unit after certain operations. However, such a traditional cloud service is troubled by growing latency. Mobile edge computing emerges in such an environment. A short distance between the edge network and end-users mitigates this problem. However, the edge network has finite resources, making it impossible to deliver all service caching requests. To this end, a strategy is required to selectively cache services on the edge cloud. This study simulates the selection of edge services with a multi-armed bandit model and conducts a comparative study to analyze the impact that different algorithms have on performance.
ISSN:0973-1318
DOI:10.23940/ijpe.22.05.p5.350358