An Evolutionary Algorithm for Multi-Objective Workflow Scheduling with Adaptive Dynamic Grouping

For workflow scheduling with complex dependencies in cloud computing environments, existing research predominantly focuses on multi-objective algorithm optimization while neglecting the critical factor of workflow topological structure. The proposed Adaptive Dynamic Grouping (ADG) strategy breaks th...

Full description

Saved in:
Bibliographic Details
Published in:Electronics (Basel) Vol. 14; no. 13; p. 2586
Main Authors: Zhang, Guochen, Zhang, Aolong, Sun, Chaoli, Ye, Qing
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.07.2025
Subjects:
Algorithms
Analysis
Bandwidths
Cloud computing
Communication
Efficiency
Energy consumption
Evolutionary algorithms
Genetic algorithms
Multiple objective analysis
Optimization algorithms
Pareto optimization
Resource allocation
Scheduling
Variables
Virtual environments
Workflow
ISSN:2079-9292, 2079-9292
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:For workflow scheduling with complex dependencies in cloud computing environments, existing research predominantly focuses on multi-objective algorithm optimization while neglecting the critical factor of workflow topological structure. The proposed Adaptive Dynamic Grouping (ADG) strategy breaks through this limitation via dual innovative mechanisms: firstly constructing a dynamic variable grouping model based on task dependencies to effectively compress decision space and reduce global search overhead and secondly introducing an adaptive resource allocation strategy that dynamically distributes execution opportunities according to variable groups’ contribution to optimization, accelerating convergence toward the Pareto frontier. The experimental results on five real-world workflows across three major cloud providers’ virtual machines demonstrate ADG’s superior performance in multi-objective optimization, including execution time, cost, and energy consumption, providing an efficient solution for cloud-based workflow scheduling.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics14132586