Cost-Effective App Data Distribution in Edge Computing

• Published in: IEEE transactions on parallel and distributed systems Vol. 32; no. 1; pp. 31 - 44
• Main Authors: Xia, Xiaoyu, Chen, Feifei, He, Qiang, Grundy, John C., Abdelrazek, Mohamed, Jin, Hai
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Business; Cloud computing; Constraints; cost-effectiveness; Data communication; data distribution; Data transmission; Distributed databases; Edge computing; edge server network; Electric power distribution; Facebook; Integer programming; Optimization; Servers; Videos; Virtual reality
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2020.3010521

Edge computing, as an extension of cloud computing, distributes computing and storage resources from centralized cloud to distributed edge servers, to power a variety of applications demanding low latency, e.g., IoT services, virtual reality, real-time navigation, etc. From an app vendor's perspective, app data needs to be transferred from the cloud to specific edge servers in an area to serve the app users in the area. However, according to the pay-as-you-go business model, distributing a large amount of data from the cloud to edge servers can be expensive. The optimal data distribution strategy must minimize the cost incurred, which includes two major components, the cost of data transmission between the cloud to edge servers and the cost of data transmission between edge servers. In the meantime, the delay constraint must be fulfilled - the data distribution must not take too long. In this article, we make the first attempt to formulate this Edge Data Distribution (EDD) problem as a constrained optimization problem from the app vendor's perspective and prove its <inline-formula><tex-math notation="LaTeX">\mathcal {NP}</tex-math> <mml:math><mml:mi mathvariant="script">NP</mml:mi></mml:math><inline-graphic xlink:href="xia-ieq1-3010521.gif"/> </inline-formula>-hardness. We propose an optimal approach named EDD-IP to solve this problem exactly with the Integer Programming technique. Then, we propose an <inline-formula><tex-math notation="LaTeX">O(k)</tex-math> <mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>k</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq2-3010521.gif"/> </inline-formula>-approximation algorithm named EDD-A for finding approximate solutions to large-scale EDD problems efficiently. EDD-IP and EDD-A are evaluated on a real-world dataset and the results demonstrate that they significantly outperform three representative approaches.