Proceedings Article10.1145/3421766.3421785
Cloud Computing Task Scheduling Based on Improved Differential Evolution Algorithm
Xueliang Fu,Yumeng Hu,Yang Sun +2 more
- 15 Oct 2020
- pp 118-124
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TL;DR: It is proved that three improved differential evolutionary cloud task scheduling algorithms have less task completion time than the traditional differential evolution algorithm, and the bigger the number of tasks, the more obvious the performance optimization of the algorithm.
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Abstract: In recent years, the introduction of intelligent optimization algorithm into cloud computing task scheduling to deal with the problem of massive task scheduling is a research hotspot. This paper proposes three improved differential evolution cloud computing task scheduling algorithms, and the application of the improved differential evolution algorithm in cloud computing task scheduling problem is mainly studied. The maximum task completion time is optimized by improving parameters F, CR, and variation strategies. Through two sets of simulation experiments, it is proved that three improved differential evolutionary cloud task scheduling algorithms have less task completion time than the traditional differential evolution algorithm, and the bigger the number of tasks, the more obvious the performance optimization of the algorithm.
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Citations
Research on Cloud Computing Adaptive Task Scheduling Based on Ant Colony Algorithm
TL;DR: Wang et al. as mentioned in this paper designed an adaptive task scheduling algorithm for cloud computing based on ant colony algorithm, and a pheromone adaptive update adjustment mechanism was added to improve the convergence speed of the algorithm and effectively avoid the emergence of local optimal solutions.
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•Journal Article
Task scheduling based on differential evolution algorithm in cloud computing
TL;DR: The MSMDE mentioned in this thesis is an algorithm based on multi-strategy mutation differential evolution of differential evolution that can effectively solve combinatorial optimization problem.
1
Optimal Resource Allocation in Cloud Computing Using Novel ACO-DE Algorithm
Himanshu Bhusan Sahoo,D. Chandrasekhar Rao +1 more
- 01 Jan 2024
TL;DR: Optimal resource allocation in cloud computing using novel ACO-DE algorithm achieves effective resource utilisation, performance optimisation, and cost optimisation.
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TL;DR: The results from this study show that DE generally outperforms the other algorithms, however, on two noisy functions, both DE and PSO were outperformed by the EA.
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TL;DR: This study reports how the differential evolution (DE) algorithm performed on the test bed developed for the CEC05 contest for real parameter optimization.
Cloud computing and its key techniques
Xu Wang,Beizhan Wang,Jing Huang +2 more
- 10 Jun 2011
TL;DR: This paper described what is cloud computing and took Google's cloud computing techniques as an example, summed up key techniques, such as data storage technology, data management technology, and programming model and task scheduling model, used in cloud computing, and some example of cloud computing vendors were illustrated and compared.
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An Improved Differential Evolution Task Scheduling Algorithm Based on Cloud Computing
Jingmei Li,Jia Liu,Jiaxiang Wang +2 more
- 01 Oct 2018
TL;DR: Comparing the IDE algorithm with the traditional differential evolution algorithm, genetic algorithm and Min-Min algorithm, the results show that IDE algorithm task completion time is short, which improves the utilization of cloud computing resource pools, and the cost of computing resources in a similar period of time is low.
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