A Survey of Low-Energy Parallel Scheduling Algorithms

High energy consumption is one of the biggest obstacles to the rapid development of computing systems, and reducing energy consumption is quite urgent and necessary for sustainable computing. Low-energy scheduling based on dynamic voltage and frequency scaling (DVFS) is one of the most commonly used...

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Published inIEEE transactions on sustainable computing Vol. 7; no. 1; pp. 27 - 46
Main Authors Xie, Guoqi, Xiao, Xiongren, Peng, Hao, Li, Renfa, Li, Keqin
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Computation
Computer architecture
Dynamic scheduling
Energy consumption
Energy management
Energy-aware
energy-conscious
energy-efficient
Game theory
Heuristic algorithms
Heuristic methods
Integer programming
Job shop scheduling
Machine learning
Machine learning algorithms
Optimization
Optimization techniques
parallel scheduling
Renewable energy
Scheduling
Scheduling algorithms
Trends
Online AccessGet full text
ISSN2377-3782
2377-3790
DOI10.1109/TSUSC.2021.3057983

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Abstract High energy consumption is one of the biggest obstacles to the rapid development of computing systems, and reducing energy consumption is quite urgent and necessary for sustainable computing. Low-energy scheduling based on dynamic voltage and frequency scaling (DVFS) is one of the most commonly used energy optimization techniques. Recent survey works have reviewed some low-energy scheduling algorithms, but there is currently no systematic review in low-energy parallel scheduling algorithms. With the increasing complexity of function requirements, many parallel applications have been executed in various sustainable computing systems. In this paper, we survey recent advances in low-energy parallel scheduling algorithms according to three scheduling styles, namely: 1) energy-efficient parallel scheduling algorithms; 2) energy-aware parallel scheduling algorithms; and 3) energy-conscious parallel scheduling algorithms. Low-energy parallel scheduling algorithms basically involve five categories of 1) heuristic algorithms; 2) meta-heuristic algorithms; 3) integer programming algorithms; 4) machine learning algorithms; and 5) game theory algorithms. Further, we introduce the future trends in low-energy parallel scheduling algorithms from the perspectives of new requirements and future developments. By surveying the recent advances and introducing the future trends, we expect to provide researchers with a systematic reference and development directions in low-energy parallel scheduling for sustainable computing systems.
AbstractList High energy consumption is one of the biggest obstacles to the rapid development of computing systems, and reducing energy consumption is quite urgent and necessary for sustainable computing. Low-energy scheduling based on dynamic voltage and frequency scaling (DVFS) is one of the most commonly used energy optimization techniques. Recent survey works have reviewed some low-energy scheduling algorithms, but there is currently no systematic review in low-energy parallel scheduling algorithms. With the increasing complexity of function requirements, many parallel applications have been executed in various sustainable computing systems. In this paper, we survey recent advances in low-energy parallel scheduling algorithms according to three scheduling styles, namely: 1) energy-efficient parallel scheduling algorithms; 2) energy-aware parallel scheduling algorithms; and 3) energy-conscious parallel scheduling algorithms. Low-energy parallel scheduling algorithms basically involve five categories of 1) heuristic algorithms; 2) meta-heuristic algorithms; 3) integer programming algorithms; 4) machine learning algorithms; and 5) game theory algorithms. Further, we introduce the future trends in low-energy parallel scheduling algorithms from the perspectives of new requirements and future developments. By surveying the recent advances and introducing the future trends, we expect to provide researchers with a systematic reference and development directions in low-energy parallel scheduling for sustainable computing systems.
Author Xiao, Xiongren
Peng, Hao
Li, Keqin
Li, Renfa
Xie, Guoqi
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Snippet High energy consumption is one of the biggest obstacles to the rapid development of computing systems, and reducing energy consumption is quite urgent and...
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SubjectTerms Algorithms
Computation
Computer architecture
Dynamic scheduling
Energy consumption
Energy management
Energy-aware
energy-conscious
energy-efficient
Game theory
Heuristic algorithms
Heuristic methods
Integer programming
Job shop scheduling
Machine learning
Machine learning algorithms
Optimization
Optimization techniques
parallel scheduling
Renewable energy
Scheduling
Scheduling algorithms
Trends
Title A Survey of Low-Energy Parallel Scheduling Algorithms
URI https://ieeexplore.ieee.org/document/9351758
https://www.proquest.com/docview/2637438109
Volume 7
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