A Data Placement Strategy Based on Genetic Algorithm for Scientific Workflows

2012 ◽  
Author(s):  
Zhao Er-Dun ◽  
Qi Yong-Qiang ◽  
Xiang Xing-Xing ◽  
Chen Yi
Keyword(s):  
Genetic Algorithm ◽  
Data Placement ◽  
Scientific Workflows

scholarly journals A parallel multi-objective genetic algorithm for scheduling scientific workflows in cloud computing

2020 ◽  
Vol 16 (8) ◽  
pp. 155014772094914
Author(s):  
Muhammad Sardaraz ◽  
Muhammad Tahir
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Scientific Workflows ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Recent Developments ◽  
Benchmark Datasets ◽  
Comparative Results

Recent developments in cloud computing have made it a powerful solution for executing large-scale scientific problems. The complexity of scientific workflows demands efficient utilization of cloud resources to satisfy user requirements. Scheduling of scientific workflows in a cloud environment is a challenge for researchers. The problem is considered as NP-hard. Some constraints such as a heterogeneous environment, dependencies between tasks, quality of service and user deadlines make it difficult for the scheduler to fully utilize available resources. The problem has been extensively studied in the literature. Different researchers have targeted different parameters. This article presents a multi-objective scheduling algorithm for scheduling scientific workflows in cloud computing. The solution is based on genetic algorithm that targets makespan, monetary cost, and load balance. The proposed algorithm first finds the best solution for each parameter. Based on these solutions, the algorithm finds the superbest solution for all parameters. The proposed algorithm is evaluated with benchmark datasets and comparative results with the standard genetic algorithm, particle swarm optimization, and specialized scheduler are presented. The results show that the proposed algorithm achieves an improvement in makespan and reduces the cost with a well load balanced system.

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