Market-Based Resource Allocation of Distributed Cloud Computing Services : Virtual Energy Storage Systems

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

7 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)22811-22821
Journal / PublicationIEEE Internet of Things Journal
Volume9
Issue number22
Online published21 Jun 2022
Publication statusPublished - 15 Nov 2022
Externally publishedYes

Abstract

The cloud-based application is a major developing feature of smart grids. Apart from centralized Internet data centers (IDCs), distributed cloud resources (CRs) can also provide cloud computing services with low latency and high reliability. For the distributed cloud computing, CRs aggregators (CRAs) will integrate the distributed idle computing resources, which are dispersed energy consumers in the system, to form virtual IDCs. This article presents a market-based computing resource allocation method for distributed cloud computing services. The computing resource allocation refers to the approach to allocating the computing workloads to different CRs. First, the batch workload scheduling (BWS)-based virtual energy storage system (VESS) model and thermal inertia (TI)-based VESS model are proposed to help CRAs better aggregate the distributed CRs and characterize the energy consumption flexibility of the virtual IDCs. Then, the energy trading behavior of the CRAs in the transactive energy market is modeled in the resource allocation process. Case studies are conducted on a 55-bus electricity system. It can be found that energy consumption costs can be reduced by applying the proposed methodology, and revenues from providing cloud computing services can be increased. © 2014 IEEE.

Research Area(s)

  • Distributed cloud computing, Internet data centers (IDC), transactive energy market, virtual energy storage

Citation Format(s)

Market-Based Resource Allocation of Distributed Cloud Computing Services: Virtual Energy Storage Systems. / Tao, Yuechuan; Qiu, Jing; Lai, Shuying et al.
In: IEEE Internet of Things Journal, Vol. 9, No. 22, 15.11.2022, p. 22811-22821.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review