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Scheduling Constrained Cloud Workflow Tasks via Evolutionary Multitasking Optimization With Adaptive Knowledge Transfer

delete2024-11-01
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PRE
AI
周
周佳军 (Jiajun Zhou)
Gao Liang cover
Gao Liang (Liang Gao) *
Y
Yun Li
DOI:10.1109/TSC.2024.3463423delete
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Abstract

Abstract

En 中文
Cloud workflow scheduling (CWS) is critical for meeting user's high performance expectations in large-scale data processing and computing applications. CWS is known to be NP-hard and needs advanced scheduling techniques. Evolutionary algorithm and heuristic-based search techniques have gained massive popularity in addressing CWS, yet they either suffer from expensive computational cost or heavily rely on domain-specific experiences, which limit their practical applications. Bearing this in mind, we develop a novel evolutionary multi-task optimization framework to tackle a group of constrained CWS tasks simultaneously with the aid of adaptive cross-task problem-solving knowledge transfer. In particular, two collaborative knowledge exchange strategies, namely, constraint-free archive strategy and cross-task evolution strategy, are devised to extract useful building blocks from foreign tasks to boost the search efficiency. Further, to leverage the cooperative effects of both strategies, we develop an adaptive switching mechanism such that appropriate knowledge transfer strategies are learned automatically according to the population evolution status. Extensive experiments are conducted on real-world applications under various conditions, the comparison results show that our proposal delivers higher quality schedules than the state-of-the-art competitors in most cases.
Keywords:
Processor scheduling
Search problems
Problem-solving
Optimal scheduling
Heuristic algorithms
Genetic algorithms
Costs
Cloud computing
workflow scheduling
knowledge transfer
multi-task optimization
multiple workflows

Journal

IEEE Transactions on Services Computing cover
IEEE Transactions on Services Computing
IF:
5.8
Papers:
2.2K
Citations:
6.5K

Organization

C
China University of Geosciences
Scholars:
3.7W
Papers: 2.8W
Citations: 4.3W
S
shenzhen institute for advanced study, uestc
Scholars:
419
Papers: 371
Citations: 1
Cited Papers

Cited Papers

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ET2FA: A Hybrid Heuristic Algorithm for Deadline-Constrained Workflow Scheduling in Cloud
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Characterizing and profiling scientific workflows
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Dynamic Auxiliary Task-Based Evolutionary Multitasking for Constrained Multiobjective Optimization
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Multiobjective Cloud Workflow Scheduling: A Multiple Populations Ant Colony System Approach
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Cost-Efficient Workflow Scheduling Algorithm for Applications With Deadline Constraint on Heterogeneous Clouds
err2022-09-01
err43
PREAI
errTang, Xiaoyong; Cao, Wenbiao; Tang, Huiya; Deng, Tan; Mei, Jing; Liu, Yi; Shi, Cheng; Xia, Meng; Zeng, Zeng
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