CRATES: A Cache Replacement Algorithm for Low Access Frequency Period in Edge Server

Pengmiao Li, Yuchao Zhang, Huahai Zhang, Wendong Wang, Ke Xu, Zhili Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In recent years, with the maturity of 5G and Internet of Things technologies, the traffic in mobile network is growing explosively. To reduce the burden of cloud data centers and CDN network, edge servers that are closer to users are widely deployed, caching hot contents and providing higher Quality of Service (QoS) by shortening access latency. Storage resources on edge servers are much limited compared with CDN servers, so the research on cache replacement strategy of edge servers is critical to edge computing and storage area. Many efforts have been made to improve caching performance on edge servers. Existing caching strategies only focus on the high access frequency period to solve the caching problem, they ignore low access frequency period with two characteristics, including that hot contents are difficult to predict and hot topics usually change unstably, which makes it inefficient to improve the hit rate on edge servers.In this paper, we deeply analyzed the real traces from Chuang-Cache and found some specific user groups are playing more important roles than general users during low access frequency period, and the contents accessed by these specific user groups have a much higher possibility to become hot contents. Therefore, we firstly classify such users to core users, and treat others as common users. Then we adopt the principal component analysis algorithm to analyze the relationship between hot contents and core users. On this basis, we finally propose a hot contents pre-cache protection mechanism, which is a significant part of our cache replacement algorithm CRATES. To improve CRATES's efficiency, we extract key part of historical data by designing a sliding window method. Through a series of experiments using real application data, we demonstrate that CRATES reaches about 98% in caching hit rate and outperforms the state-of-the-art algorithm LRB by 1.4X.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 17th International Conference on Mobility, Sensing and Networking, MSN 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages128-135
Number of pages8
ISBN (Electronic)9781665406680
DOIs
StatePublished - 2021
Event17th International Conference on Mobility, Sensing and Networking, MSN 2021 - Virtual, Exeter, United Kingdom
Duration: Dec 13 2021Dec 15 2021

Publication series

NameProceedings - 2021 17th International Conference on Mobility, Sensing and Networking, MSN 2021

Conference

Conference17th International Conference on Mobility, Sensing and Networking, MSN 2021
Country/TerritoryUnited Kingdom
CityVirtual, Exeter
Period12/13/2112/15/21

Bibliographical note

Funding Information:
The work was supported in part by the National Natural Science Foundation of China (NSFC) Youth Science Foundation under Grant 61802024, BUPT-ChuangcacheJoint Laboratory under B2020009, the Key Project of Beijing Natural Science Foundation under M21030, the Fundamental Research Funds for the Central Universities under Grant 2482020RC36, the NSFC under Grant 62072047, and the NSFC under Grant 62072048. The work of Pengmiao Li was supported in part by the BUPT Excellent Ph.D. Students Foundation under CX2019134.

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Edge caching
  • and hit rate
  • core user
  • hot contents

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