A Cloud Environment Resource Scheduling Strategy Based On Improvrd Ant Colony Algorithm

Cloud computing is an emerging computing model developed with grid computing, which combined the existing mature virtualization technology, virtualizing various resources of network into a huge virtual resource pool, to perform different tasks of different users by organizing virtual resources. Therefore, a reasonable resource scheduling strategy will have a major impact on the performance of the entire cloud platform.This paper studied a lot of scheduling algorithms in cloud computing environment, and ant colony algorithm has the characteristics of distribution, heterogeneity and low demand for the initial conditions, which makes it very suitable for resource scheduling in cloud computing environment. However, the standard ant colony algorithm also has shortcomings. If the algorithm will apply to the actual cloud platform, we must improve it Due to the problems that it always accompanied that the load of resource nodes is imbalance and the resource utilization rate is not high in the process of resource scheduling based on the standard ant colony algorithm, this paper innovatively designed a pretreatment mechanism of cracking factors and proposed a resource scheduling strategy based on improved ant colony algorithm.This paper designs a dynamic, eclectic method in the process of solving the scope of cracking factors, mainly considering two aspects:first, the cloud resource scheduling has dynamic characteristics, i.e. there will be a new node into the cloud at any time, and some nodes out for some reason; Second, the value of degradation factors can’t be too big or too small So-called dynamic, is aimed at different scenarios to dynamically select the value range of degradation factors; So-called eclectic, is that set the middle value of 0.5 firstly, then alternately set bigger and smaller values near 0.5 to ensure the value range of degradation factors.In this paper, do the simulation experiment with the aid of VC++6.0 platform, and analysis the simulation datas through MATLAB. Simulation experiment is divided into two steps:the first step is degradation factors simulation; the second step is the improved algorithm and the standard ant colony algorithm simulation The experimental results show that within the scope of the value of degradation factor, the resource scheduling strategy based on improved ant colony algorithm has better load balance and better resource utilization, at the same time, improved ant colony algorithm shortens the average time of task execution and has the stronger ability of global searching.