Constrained app data caching over edge server graphs in edge computing environment

Xia, Xiaoyu, Chen, Feifei, Grundy, John, Abdelrazek, Mohamed, Jin, Hai and He, Qiang 2021, Constrained app data caching over edge server graphs in edge computing environment, IEEE transactions on services computing, pp. 1-13, doi: 10.1109/tsc.2021.3062017.

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Title Constrained app data caching over edge server graphs in edge computing environment
Author(s) Xia, XiaoyuORCID iD for Xia, Xiaoyu
Chen, FeifeiORCID iD for Chen, Feifei
Grundy, John
Abdelrazek, MohamedORCID iD for Abdelrazek, Mohamed
Jin, Hai
He, Qiang
Journal name IEEE transactions on services computing
Start page 1
End page 13
Total pages 13
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2021-02-24
ISSN 2372-0204
Keyword(s) edge computing
data caching
approximation algorithm
Summary In recent years, edge computing, as an extension of cloud computing, has emerged as a promising paradigm for powering a variety of applications demanding low latency, e.g., virtual or augmented reality, interactive gaming, real-time navigation, etc. In the edge computing environment, edge servers are deployed at base stations to offer highly-accessible computing capacities to nearby end-users, e.g., CPU, RAM, storage, etc. From a service provider's perspective, caching app data on edge servers can ensure low latency in its users' data retrieval. Given constrained cache spaces on edge servers due to their physical sizes, the optimal data caching strategy must minimize overall user latency. In this paper, we formulate this Constrained Edge Data Caching (CEDC) problem as a constrained optimization problem from the service provider's perspective and prove its NP-hardness. We propose an optimal approach named CEDC-IP to solve this CEDC problem exactly with the Integer Programming technique. We also provide an approximation algorithm named CEDC-A for finding approximate solutions to large-scale CEDC problems efficiently and prove its approximation ratio. CEDC-IP and CEDC-A are evaluated on a real-world data set and a synthesized data set. The results demonstrate that they significantly outperform four representative approaches.
Language eng
DOI 10.1109/tsc.2021.3062017
Indigenous content off
Field of Research 0803 Computer Software
0805 Distributed Computing
0806 Information Systems
HERDC Research category C1 Refereed article in a scholarly journal
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