Temporal-variation-aware profit-maximized and delay-bounded task scheduling in green data center

Document Type

Conference Proceeding

Publication Date

1-1-2019

Abstract

An increasing number of enterprises deploy their business applications in green data centers (GDCs) to address irregular and drastic natures in task arrival of global users. GDCs aim to schedule tasks in the most cost-effective way, and achieve the profit maximization by increasing green energy usage and reducing brown one. However, prices of power grid, revenue, solar and wind energy vary dynamically within tasks’ delay constraints, and this brings a high challenge to maximize the profit of GDCs such that their delay constraints are strictly met. Different from existing studies, a Temporal-variation-aware Profit-maximized Task Scheduling (TPTS) algorithm is proposed to consider dynamic differences, and intelligently schedule all tasks to GDCs within their delay constraints. In each interval, TPTS solves a constrained profit maximization problem by a novel Simulated-annealing-based Chaotic Particle swarm optimization (SCP). Compared to several state-of-the-art scheduling algorithms, TPTS significantly increases throughput and profit while strictly meeting tasks’ delay constraints.

Identifier

85075874341 (Scopus)

ISBN

[9783030349134]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/978-3-030-34914-1_20

e-ISSN

16113349

ISSN

03029743

First Page

203

Last Page

212

Volume

11874 LNCS

Grant

61703011

Fund Ref

National Natural Science Foundation of China

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