A Game Theoretical Balancing Approach for Offloaded Tasks in Edge Datacenters

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

7 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS 2022)
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages526-536
ISBN (electronic)978-1-6654-7177-0
Publication statusPublished - 2022

Publication series

NameProceedings - International Conference on Distributed Computing Systems
Volume2022-July

Conference

Title42nd IEEE International Conference on Distributed Computing Systems, ICDCS 2022
PlaceItaly
CityBologna
Period10 - 13 July 2022

Abstract

Edge computing is the next-generation computing paradigm that brings the processing capability closer to the location where it is needed. 5G and beyond 5G aim to achieve substantial improvement for the performance of edge computing in terms of e.g. higher throughput and lower latency. Smart base stations are often attached with edge datacenters consisting of many edge servers equipped with computing and storage capabilities. These servers are used to execute offloaded tasks from edge equipment such as Internet of Things. It is important to have an efficient offloading algorithm that can guarantee specific service-level objectives (SLOs) by assigning tasks to appropriate edge servers. Traditional offloading schemes such as static and learning-based algorithms either have limited performance or result in high overhead for task assignment to servers. In this paper, we propose an efficient game-theoretical scheduling algorithm for offloaded tasks at edge datacenters. The core contribution of the algorithm is to design a public goods investment model for edge servers. Based on the model, we design a lightweight scheduling algorithm to reduce the average load of edge servers and enhance the stability of edge datacenter systems. Experimental results demonstrate the significant benefits of the proposed algorithm in reducing the response latency of tasks and balancing the workload of edge servers.

Research Area(s)

  • Edge computing, Game theory, Load balancing, Offloading, Public goods model

Citation Format(s)

A Game Theoretical Balancing Approach for Offloaded Tasks in Edge Datacenters. / Lu, Hongli; Xu, Guangping; Sung, Chi Wan et al.
Proceedings - 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS 2022). Institute of Electrical and Electronics Engineers, Inc., 2022. p. 526-536 (Proceedings - International Conference on Distributed Computing Systems; Vol. 2022-July).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review