An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments (original) (raw)

2019, Neural Computing and Applications

Cloud computing is an emerging distributed system that provides flexible and dynamically scalable computing resources for use at low cost. Task scheduling in cloud computing environment is one of the main problems that need to be addressed in order to improve system performance and increase cloud consumer satisfaction. Although there are many task scheduling algorithms, existing approaches mainly focus on minimizing the total completion time while ignoring workload balancing. Moreover, managing the quality of service (QoS) of the existing approaches still needs to be improved. In this paper, we propose a novel algorithm named MGGS (modified genetic algorithm (GA) combined with greedy strategy). The proposed algorithm leverages the modified GA algorithm combined with greedy strategy to optimize task scheduling process. Different from existing algorithms, MGGS can find an optimal solution using fewer number of iterations. To evaluate the performance of MGGS, we compared the performance of the proposed algorithm with several existing algorithms based on the total completion time, average response time, and QoS parameters. The results obtained from the experiments show that MGGS performs well as compared to other task scheduling algorithms.

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