An Optimization Model Design of Distributed Cloud Computing Based on Genetic Algorithm

How to use existing resources to allocate and schedule cloud tasks reasonably and improve the utilization rate of cloud computing resources has become a bottleneck in the development of cloud computing platform. In this paper, an optimization model of distributed cloud computing based on improved GA (Genetic Algorithm) is proposed. The coding mode code based on the combination of binary coding and floating-point coding is adopted, and any scheduling scheme has a corresponding coding scheme, which can ensure the completeness of the search space. At the same time, layered technology is added to individual decoding and initialization population. Simulation results show that the scheduling time of this algorithm is shorter, and the advantages of this algorithm will be more obvious with the increase of the number of cloud tasks. In this paper, the standard deviation of task running time on each computing resource is used to optimize the load balance, and the effect is good. The improved GA in this paper is effective, which can effectively reduce the probability of the algorithm falling into local optimum, and the algorithm is less likely to fall into local optimum in the initial stage and converges faster. Experimental results show that this algorithm is superior to adaptive GA, and it is an effective TS (Task scheduling) algorithm in distributed cloud computing environment.