A Two-Stage Estimation of Distribution Algorithm With Heuristics for Energy-Aware Cloud Workflow Scheduling

With the enormous increase in energy usage by cloud data centers for handling various workflow applications, the energy-aware cloud workflow scheduling has become a hot issue. However, there is still a need and room for improvement in both the model for estimating workflow energy consumption and the...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on services computing Vol. 16; no. 6; pp. 4183 - 4197
Main Authors: Xie, Yi, Wang, Xue-Yi, Shen, Zi-Jun, Sheng, Yu-Han, Wu, Gong-Xing
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Cloud computing
Data centers
Energy consumption
Energy management
Estimation
estimation of distribution algorithm
Heuristic methods
Job shop scheduling
Processor scheduling
Scheduling
Search problems
Task analysis
Transmission line measurements
Workflow
Workflow software
ISSN:1939-1374, 2372-0204
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the enormous increase in energy usage by cloud data centers for handling various workflow applications, the energy-aware cloud workflow scheduling has become a hot issue. However, there is still a need and room for improvement in both the model for estimating workflow energy consumption and the algorithm for energy-aware cloud workflow scheduling. To fill these gaps, a new model for estimating the energy consumption of the cloud workflow execution and a novel Two-Stage Estimation of Distribution Algorithm with heuristics (TSEDA) for energy-aware cloud workflow scheduling are proposed based on the relationships among scheduling scheme, host load and power. In particular, in the proposed TSEDA, a new probability model and its updating mechanism are presented, and a two-stage coevolution strategy with some novel heuristic methods for individual generation, decoding and improvement is designed. Extensive experiments are conducted on workflow applications with various sizes and types, and the results show that the proposed TSEDA outperforms conventional algorithms.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1939-1374
2372-0204
DOI:10.1109/TSC.2023.3311785