"Geography-Aware Task Scheduling for Profit Maximization in Distributed" by Haitao Yuan, Jing Bi et al.
 

Geography-Aware Task Scheduling for Profit Maximization in Distributed Green Data Centers

Document Type

Article

Publication Date

1-1-2022

Abstract

Infrastructure in Distributed Green Data Centers (DGDCs) is concurrently shared by multiple different applications to flexibly provide a growing number of services to global users in a cost-effective way. A highly challenging problem is how to maximize the total profit of the DGDC provider in a market where Internet Service Provider (ISP) bandwidth price, availability of green energy, price of power grid, and revenue brought by the execution of tasks all vary with geographical locations. Unlike existing studies, this article proposes a Geography-Aware Task Scheduling (GATS) approach by considering spatial variations in DGDCs to maximize the total profit of the DGDC provider by intelligently scheduling tasks of all applications. In each time slot, the formulated profit maximization problem is solved as a convex optimization one via the interior point method. Trace-driven simulations show that GATS achieves larger total profit and higher throughput than two typical task scheduling approaches.

Identifier

85139448048 (Scopus)

Publication Title

IEEE Transactions on Cloud Computing

External Full Text Location

https://doi.org/10.1109/TCC.2020.3001051

e-ISSN

21687161

First Page

1864

Last Page

1874

Issue

3

Volume

10

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