원문정보
초록
영어
The resource scheduling imbalance is a multi-objective optimization problem in cloud computing environment, this paper introduced the particle swarm optimization algorithm in cloud computing, a simulated annealing ideas is proposed in view of the prematurity of the algorithm, on the premise of performance determination, the position of the particle is determined by the probability choice, which helps the particle to escape. In order to enhance the global searching ability of the particle, the algorithm is combined with the chaotic mechanism to improve the accuracy of the algorithm. The inertia weight is adjusted dynamically according to the current state of the particle, accordingly, at the same time to obtain the optimal solution to ensure the convergence. Analysis of the experimental results show that the improved algorithm has a significant improvement in the ability of optimization and convergence speed, compared with other algorithms, the benchmark functions comparison is better, the different resource task proportion spent the shortest time and load balancing is the highest.
목차
1. Introduction
2. The Cloud Computing Resource Scheduling Problem Description
3. Particle Swarm Optimization Algorithm
3.1. Basic Particle Swarm Optimization Algorithm
3.2. Chaos Mechanism
3.3. Analysis of Inertia Weight
3.4. Particle Performance Analysis
3.5. Simulated Annealing
4. The Cloud Computing Resource Scheduling Strategy Based on Improved Particle Swarm Optimization
4.1. Coding Strategy
4.2. Fitness Function and Algorithm Step
5. Experimental Analysis
6. Conclusion
References