Formulating Cost-Effective Data Distribution Strategies Online for Edge Cache Systems

Article


Xia, Xiaoyu, Chen, Feifei, He, Qiang, Grundy, John, Abdelrazek, Mohamed, Shen, Jun, Bouguettaya, Athman and Jin, Hai. 2022. "Formulating Cost-Effective Data Distribution Strategies Online for Edge Cache Systems." IEEE Transactions on Parallel and Distributed Systems. 33 (12), pp. 4270-4281. https://doi.org/10.1109/TPDS.2022.3185250
Article Title

Formulating Cost-Effective Data Distribution Strategies Online for Edge Cache Systems

ERA Journal ID20981
Article CategoryArticle
AuthorsXia, Xiaoyu (Author), Chen, Feifei (Author), He, Qiang (Author), Grundy, John (Author), Abdelrazek, Mohamed (Author), Shen, Jun (Author), Bouguettaya, Athman (Author) and Jin, Hai (Author)
Journal TitleIEEE Transactions on Parallel and Distributed Systems
Journal Citation33 (12), pp. 4270-4281
Number of Pages12
Year2022
Place of PublicationUnited States
ISSN1045-9219
1558-2183
Digital Object Identifier (DOI)https://doi.org/10.1109/TPDS.2022.3185250
Web Address (URL)https://ieeexplore.ieee.org/document/9804351
Abstract

Edge Computing (EC) enables a new kind of caching system in close geographic proximity to end-users by allowing app vendors to cache popular data on edge servers deployed at base stations. This edge cache system can better support latency-sensitive applications. However, transmitting data from the centralized cloud to the edge servers without proper transmission strategies may cost app vendors dearly. Cost-effective data distribution strategies are of particular importance for applications, whose data to be cached at the edge often changes dynamically. In this paper, we study this Online Edge Data Distribution (OEDD) problem, aiming to minimize app vendors’ total transmission cost, while ensuring low transmission latency in the long term. We first model this problem and prove its NP-hardness. We then combine Lyapunov optimization and game theory to propose a novel Latency-Aware Online (LAO) approach for solving this OEDD problem over time in a distributed manner with provable performance guarantees. The evaluation of LAO based on a real-world dataset demonstrates that it can help app vendors formulate cost-effective edge data distribution strategies in an online manner.

KeywordsAustralia; Cloud computing; Costs; Data communication; data distribution; edge cache system; online algorithm; optimization; Optimization; Servers; Videos
ANZSRC Field of Research 2020460503. Data models, storage and indexing
460605. Distributed systems and algorithms
460612. Service oriented computing
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Byline AffiliationsUniversity of Adelaide
Swinburne University of Technology
Monash University
Deakin University
University of Sydney
University of Wollongong
Huazhong University of Science and Technology, China
Institution of OriginUniversity of Southern Queensland
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