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https://hdl.handle.net/2440/138644
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Type: | Journal article |
Title: | Constrained App Data Caching over Edge Server Graphs in Edge Computing Environment |
Author: | Xia, X. Chen, F. Grundy, J. Abdelrazek, M. Jin, H. He, Q. |
Citation: | IEEE Transactions on Services Computing, 2022; 15(5):2635-2647 |
Publisher: | Institute of Electrical and Electronics Engineers |
Issue Date: | 2022 |
ISSN: | 1939-1374 1939-1374 |
Statement of Responsibility: | Xiaoyu Xia, Feifei Chen, John Grundy, Mohamed Abdelrazek, Hai Jin, and Qiang He |
Abstract: | 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. |
Keywords: | Edge computing; data caching; optimization; approximation algorithm |
Rights: | © 2021 IEEE |
DOI: | 10.1109/TSC.2021.3062017 |
Grant ID: | http://purl.org/au-research/grants/arc/DP180100212 http://purl.org/au-research/grants/arc/DP200102491 http://purl.org/au-research/grants/arc/FL190100035 |
Published version: | http://dx.doi.org/10.1109/tsc.2021.3062017 |
Appears in Collections: | Computer Science publications |
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