Study on Resources Scheduling Based on ACO Allgorithm and PSO Algorithm in Cloud Computing

Wen, Xiaotang; Huang, Minghe; Shi, Jianxin

doi:10.1109/dcabes.2012.63

Cited by 55 publications

(17 citation statements)

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“…That is, in every ant step their approach does not try to select the light-load resource to make load balance, but to select the heavy-load resource to maximize the resource utilization. Wen et al [2012] also improved the cloud resources utilization ratio by scheduling the cloud resources based on a hybrid algorithm consisting of ACO and PSO. Their approach uses ACO as the main process to select suitable resources for different tasks.…”

Section: Scheduling For Provider Efficiencymentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

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Section: Scheduling For Provider Efficiencymentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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“…Each iteration in PSO, the movement of a particle's solutions is determined by 3 parts. First part is the current state of this particle; the second part is self-cognition -the movement of a particle's position is influenced by the Best Particle Position of this particle; and the third part is group-cognition -the movement of a particle's position is influenced by the Best Group Position [10]. Feasible solutions are changed by using genetic cross in genetic algorithm.…”

Section: Movement Of Particles' Positionsmentioning

confidence: 99%

“…‫ݔݏ݁ݏ‪݇ܿℎ‬ݐ݊ܽ‬ 0 ‫ݏ݊݅ݐ݅݀݊ܿݎ݁‪ℎ‬ݐ‬ (10) where ρ ∈ [0,1) is the pheromone evaporation coefficient, so (1-ρ) is pheromone residue coefficient. ‫ܽݐܽ݀‬ ሺ‫ݐ‬ሻ in the Equation.…”

Section: Spreading Pheromonementioning

confidence: 99%

A Cloud Computing Resource Scheduling Method Based on Particle Swarm Optimization and Ant Colony Optimization

Xu¹,

Liu²,

Jiang³

et al. 2017

dtetr

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Cloud computing system needs to process a large amount of data and has arduous tasks. An important question in cloud computing research is how to scheduling the resource rationally in this vast system. This paper presents an algorithm of resource scheduling combining Particle Swarm Optimization and Ant Colony Optimization. Firstly, get a good initial solution quickly through PSO algorithm, then ACO use the initial solution to determine the generation of initial pheromone distribution which is used to calculate the optimal solution through iteration. By experiment, the scheduling algorithm which combines PSO and ACO is better than both of the algorithms based on PSO or ACO.

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“…However, many techniques and strategies were developed to improve the PSO for task scheduling. For example, [13] proposed an algorithm that combined the ACO and PSO algorithms in order to improve the performance. This combination improved the convergence speed and the resource utilization ratio.…”

Section: Related Workmentioning

confidence: 99%