원문정보
초록
영어
With the development of cloud computing technology, people not only want to pursue the shortest time to complete the tasks by using cloud computing, but also hope to take into the running costs of machines. Existing task scheduling algorithm in the cloud computing environment has been unable to meet people's needs. As an extension and generalization of the model checking theory, probability model checking is also used in many fields, such as random distributed algorithm and other areas. The task scheduling algorithm based on the particle swarm optimization algorithm combined with probability model is proposed in this paper. The algorithm defines the fitness functions of the time cost and the running cost. The fitness functions can improve the efficiency of the cloud computing platform. At the same time, the probability model can be used to analyze the running states of machines and the computing capability of the nodes in the cloud cluster. The probability, which is calculated by the probability model, provides the basis for changing particle swarm algorithm’s the inertia factor and the learning factor, so as to solve the drawback that the inertia factor and the learning factor solely depend on the fixed value.
목차
1. Introduction
1.1. The Present and Problems of Task Scheduling
1.2. The Present and Problems of Particle Swarm Optimization Algorithm
2. The Basic Idea of PSO Algorithm
2.1. Inertial Factor
2.2. Learning Factor
3. Improvement of PSO Algorithm based on Probability Model
3.1. Tasks Encoding
3.2. Fitness Function
3.3. Construction and Calculation of Probability Model
4. Experimental Results and Examples
4.1. Experiment and Analysis of the Algorithms
4.2. The Example of the Cloud Rendering Project
5. Conclusions
References