Cloud computing task scheduling strategy based on differential evolution and ant colony optimization

Ge, Junwei; Cai, Yu; Fang, Yiqiu

doi:10.1063/1.5039103

Cited by 1 publication

(1 citation statement)

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“…The algorithm provides resource search and allocation in a cloud computing environment efficiently, according to experimental results. Another work presented by Ge et al 24 demonstrated the DE‐ACO approach obtained by combining differential evolution algorithm (DE) and ant colony optimization (ACO), which uses cost, load balancing, and the minimum task completion time as evaluation criteria. The study was performed in a Cloudsim simulator environment, and the results were compared with min–min and ACO algorithms.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic task scheduling algorithms for cloud computing environments

Aktan

Bulut

2021

Concurrency and Computation

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Cloud computing has the advantage of providing flexibility, high-performance, pay-as-you-use, and on-demand service. One of the important research issues in cloud computing is task scheduling. The purpose of scheduling is to assign tasks to available resources while providing optimization on some objectives. Tasks have diversified characteristics, and resources are heterogeneous. These properties make task scheduling an NP-complete problem. In this study, metaheuristic and hybrid metaheuristic algorithms are developed for task scheduling problems in cloud computing environments. We have developed genetic algorithm (GA), differential evolution (DE), and simulated annealing (SA) based metaheuristic algorithms, which are also combined with a greedy approach (GR). In addition to this, we have developed hybrid metaheuristics algorithms, called DE-SA and GA-SA, which are also combined with a greedy approach. The proposed approaches are evaluated in terms of completion time and load balancing of virtual machines. In terms of average completion time, as the number of tasks increases, it has been observed that the DESA algorithm outperforms the solely used DE and SA algorithms. In addition, experiments show that hybrid algorithms improve both the average completion time and the average standard deviation of virtual machine loads for some task groups.

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Section: Introductionmentioning

confidence: 99%