Energy aware multi objective genetic algorithm for task scheduling in cloud computing

2018 ◽  
Vol 11 (4) ◽  
pp. 242
Author(s):  
C. Shoba Bindu ◽  
G.B. Hima Bindu ◽  
K. Ramani
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Energy Aware ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Energy-Aware Production Scheduling in Flow Shop and Job Shop Environments Using a Multi-Objective Genetic Algorithm

2019 ◽  
Vol 31 (2) ◽  
pp. 82-97
Author(s):  
Pablo Vallejos-Cifuentes ◽  
Camilo Ramirez-Gomez ◽  
Ana Escudero-Atehortua ◽  
Elkin Rodriguez Velasquez
Keyword(s):  
Genetic Algorithm ◽  
Production Scheduling ◽  
Job Shop ◽  
Flow Shop ◽  
Energy Aware ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

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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