IPSO: Improved Particle Swarm Optimization Based Task Scheduling at the Cloud Data Center
| Autor: | Hongtao Liu, Guoxi Ma, Qinghuang Deng, Zhiyong Luo, Leng Han |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
education.field_of_study
Mathematical optimization business.industry Computer science Population Particle swarm optimization 020206 networking & telecommunications Cloud computing 02 engineering and technology computer.software_genre Scheduling (computing) Local optimum Grid computing Utility computing CloudSim 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing education business computer |
| Zdroj: | SKG |
| DOI: | 10.1109/skg49510.2019.00032 |
| Popis: | Today, cloud computing has become an advanced form of distributed computing, grid computing, utility computing, and virtualization. Efficient task scheduling algorithms help to reduce the number of virtual machines used, thus reducing costs and improving stability. To solve the problem of cloud computing task scheduling, an improved particle swarm optimization (IPSO) task scheduling method is proposed based on the traditional PSO algorithm. Firstly, this paper describes the mathematical model of cloud computing task scheduling and the basic principle of particle swarm optimization. On this basis, the random method is used to generate the initial population definition appropriateness function, the indirect coding method is used to encode the resources, and the time-varying method is used to adjust the inertia weight. In the position update, according to the inertia weight w, the individual optimal value Pbest or the group optimal value Gbest is legalized to determine the update method of the particle velocity and position, thereby increasing the degree of discretization of the PSO algorithm. The simulation test on the CloudSim platform shows that the scheduling strategy is effective and efficient. Experimental results demonstrate that the proposed method obtains better scheduling results. Thereby controlling global search and local search, try to avoid falling into local optimum. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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