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

Haitao Yuan, Jing Bi, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1864-1874
Number of pages11
JournalIEEE Transactions on Cloud Computing
Volume10
Issue number3
DOIs
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Keywords

  • Green data centers
  • convex optimization
  • distributed computing
  • profit maximization
  • task scheduling

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