Research On Optimization And Application Of Resource Scheduling Under Cloud Environment

In recent years, with the growing size of the Internet network, the Internet traffic also needs to deal with this rapid growth. Cloud computing and other related applications are generated in this background. However, resource scheduling policy in existing cloud environments lacks of effective mechanisms to receive the task, which leads to the resource service provider neither maximizes resource utilization QoS constraints effectively nor achieves the maximum benefit. To seek out for an advanced resource scheduling algorithm, this paper first analyzes the background and significance of cloud computing research, and then introduces some main technologies of cloud computing, then, puts forward an optimization algorithm of resource scheduling in cloud environment. The three specific contributions of this paper are as follows:(1) The application requirements of optimize resource scheduling algorithm based on cloud environment are given in this paper, and the overall architecture of the cloud environment platform system in order to achieve the resource scheduling optimization algorithm is built.(2) An optimization algorithm of resource scheduling(OARS) in cloud environment is proposed based on the research of related resource scheduling algorithms. This algorithm is based on the strategy of proportion fitness selection and elitist strategy. By using the proportion of fitness selection strategy, OARS can get the proportion of fitness under the cloud platform, select the resources, and derive a new set of resources from the original resource population by crossover and mutation operation. According to elitist strategy, OARS can further optimize the traditional resource scheduling by introducing the phase-out mechanism of survival of the fittest genetic algorithm.(3) To evaluate the OARS algorithm, the comparison among the OARS with the resource scheduling algorithm without optimization and traditional scheduling genetic algorithms(GA) under the same experimental data is implemented in this paper. The experimental results show that the OARS in this paper can increase the utilization rate of resources and decrease the scheduling time so that OARS is able to convergence to the optimal solution.OARS can achieve the optimal resource scheduling because a resource with a smaller fitness is able to be selected to retain the best and direct resources to the offspring by combining the strategy of proportion fitness selection and elitist strategy. Thus the OARS not only has faster convergence rate and higher resource utilization for solving resource scheduling under cloud platform, but also provides an important reference for other studies of cloud computing.