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

Haitao Yuan; Jing Bi; Meng Chu Zhou

doi:10.1007/978-3-030-34914-1_20

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

Haitao Yuan, Jing Bi, Meng Chu Zhou

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Internet and Distributed Computing Systems 12th International Conference, IDCS 2019, Proceedings
Editors	Raffaele Montella, Angelo Ciaramella, Giancarlo Fortino, Antonio Guerrieri, Antonio Liotta
Publisher	Springer
Pages	203-212
Number of pages	10
ISBN (Print)	9783030349134
DOIs	https://doi.org/10.1007/978-3-030-34914-1_20
State	Published - 2019
Event	12th International Conference on Internet and Distributed Computing Systems, IDCS 2019 - Naples, Italy Duration: Oct 10 2019 → Oct 12 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11874 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Internet and Distributed Computing Systems, IDCS 2019
Country/Territory	Italy
City	Naples
Period	10/10/19 → 10/12/19

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Keywords

Chaotic search
Green computing
Hybrid clouds
Particle swarm optimization
Profit maximization
Simulated annealing

Access to Document

10.1007/978-3-030-34914-1_20

Cite this

Yuan, H., Bi, J., & Zhou, M. C. (2019). Temporal-variation-aware profit-maximized and delay-bounded task scheduling in green data center. In R. Montella, A. Ciaramella, G. Fortino, A. Guerrieri, & A. Liotta (Eds.), Internet and Distributed Computing Systems 12th International Conference, IDCS 2019, Proceedings (pp. 203-212). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11874 LNCS). Springer. https://doi.org/10.1007/978-3-030-34914-1_20

Yuan, Haitao ; Bi, Jing ; Zhou, Meng Chu. / Temporal-variation-aware profit-maximized and delay-bounded task scheduling in green data center. Internet and Distributed Computing Systems 12th International Conference, IDCS 2019, Proceedings. editor / Raffaele Montella ; Angelo Ciaramella ; Giancarlo Fortino ; Antonio Guerrieri ; Antonio Liotta. Springer, 2019. pp. 203-212 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ffb507d57d11450992c7d54416b59084,

title = "Temporal-variation-aware profit-maximized and delay-bounded task scheduling in green data center",

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{\textquoteright} 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{\textquoteright} delay constraints.",

keywords = "Chaotic search, Green computing, Hybrid clouds, Particle swarm optimization, Profit maximization, Simulated annealing",

author = "Haitao Yuan and Jing Bi and Zhou, {Meng Chu}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 12th International Conference on Internet and Distributed Computing Systems, IDCS 2019 ; Conference date: 10-10-2019 Through 12-10-2019",

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Yuan, H, Bi, J & Zhou, MC 2019, Temporal-variation-aware profit-maximized and delay-bounded task scheduling in green data center. in R Montella, A Ciaramella, G Fortino, A Guerrieri & A Liotta (eds), Internet and Distributed Computing Systems 12th International Conference, IDCS 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11874 LNCS, Springer, pp. 203-212, 12th International Conference on Internet and Distributed Computing Systems, IDCS 2019, Naples, Italy, 10/10/19. https://doi.org/10.1007/978-3-030-34914-1_20

Temporal-variation-aware profit-maximized and delay-bounded task scheduling in green data center. / Yuan, Haitao; Bi, Jing; Zhou, Meng Chu.
Internet and Distributed Computing Systems 12th International Conference, IDCS 2019, Proceedings. ed. / Raffaele Montella; Angelo Ciaramella; Giancarlo Fortino; Antonio Guerrieri; Antonio Liotta. Springer, 2019. p. 203-212 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11874 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Zhou, Meng Chu

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

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Yuan H, Bi J, Zhou MC. Temporal-variation-aware profit-maximized and delay-bounded task scheduling in green data center. In Montella R, Ciaramella A, Fortino G, Guerrieri A, Liotta A, editors, Internet and Distributed Computing Systems 12th International Conference, IDCS 2019, Proceedings. Springer. 2019. p. 203-212. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-34914-1_20

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

Abstract

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All Science Journal Classification (ASJC) codes

Keywords

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