Open AccessResearch on load balancing of parallel component programs based on quantum particle swarm optimization algorithm
Author(s) -
Yunfeng Peng,
Guowei Gao,
Congming Shi,
Hai Liu,
Jianan Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2132/1/012014
Subject(s) - particle swarm optimization , load balancing (electrical power) , computer science , component (thermodynamics) , multi swarm optimization , swarm intelligence , algorithm , mathematical optimization , metaheuristic , parallel algorithm , distributed computing , parallel computing , mathematics , physics , geometry , thermodynamics , grid
Parallel component applications are often deployed on heterogeneous clusters. Load balancing is very important for their performance requirement. Existing load balancing methods have high performance cost and poor balance effect. Based on the analysis of structures of parallel component applications, we established the mathematical model of load balancing for parallel components on heterogeneous clusters. We use the quantum particle swarm optimization algorithm to search the optimal solution of the proposed mathematical model and determine the best load balancing scheme. Comparing with the methods based on real-time detection and other swarm intelligence optimization algorithms, our method has lower balance cost, less number of iterations and better performance.