Abstract
Motivated by the presence of multiple caches and the non-negligible fetching latency in practical scenarios, we study the online file caching problem on multiple caches in latency-sensitive systems, e.g., edge computing. Our goal is to minimize the total latency for all file requests, where a file request can be served by a hit locally, fetching from the cloud data center, a delayed hit, relaying to other caches, or bypassing to the cloud. We propose a file-weight-based algorithm, named OnMuLa, to support delayed hits, relaying and bypassing. We conduct extensive simulations on Google’ trace and a benchmark YCSB. The results show that our algorithms significantly outperform the existing methods consistently in various experimental settings. Compared with the state-of-the-art scheme supporting multiple caches and bypassing, OnMuLa can reduce the latency by \(14.77\%\) in Google’s trace and \(49.69\%\) in YCSB.
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The work is partially supported by NSFC under Grant 62132009, and the Fundamental Research Funds for the Central Universities at China.
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Li, G., Zhang, C., Ni, H., Tan, H. (2023). Online File Caching on Multiple Caches in Latency-Sensitive Systems. In: Dinh, T.N., Li, M. (eds) Computational Data and Social Networks . CSoNet 2022. Lecture Notes in Computer Science, vol 13831. Springer, Cham. https://doi.org/10.1007/978-3-031-26303-3_26
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DOI: https://doi.org/10.1007/978-3-031-26303-3_26
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