Research On Resource Scheduling Algorithm Of Virtualzation Server Based On Genetic Algorithm

In recent years,with the rapid development of cloud computing technology,it provides a better solution for solving the problems of high purchase cost,slow system deployment,low maintenance efficiency and so on.Considering the particularity of a bank as a financial industry,combined with the actual situation and the need of paper writing,this paper starts from the construction of a private cloud based on virtualization technology inside the company to carry out the research work of this paper.The paper needs to solve the following two problems:First,resource scheduling of virtual server.Resource scheduling in cloud environment is directly related to computing cost and efficiency.The complexity and high cost of scheduling algorithm will make the implementation of the project meaningless,which is also the primary problem to be solved in this paper.Second,load balancing of computing resources.The load imbalance of virtual resource nodes is easy to appear in cloud computing resource scheduling,and the cost of time is high.From the end result,this is another problem that must be addressed in order to improve the overall performance of the serve.Genetic Algorithm as a global search heuristic algorithm to solve the optimization problem,it has great advantages in solving the above problems.It can design any fitness function according to the need to obtain the optimal solution of the problem.In order to solve the problems of slow convergence,premature maturity and unbalanced resource load in cloud computing resource scheduling,this paper improves the traditional algorithm as follows:First,optimization of initial population formation conditions.Eliminate some unreasonable genes at the time of population generation,thus reducing the execution time of the algorithm.Second,in the fitness function,the constraint cosearch nditions for load balancing of resource scheduling are added,so that the final results are more in line with the actual needs.Third,optimize the number of iterations of the algorithm in a targeted manner.According to the different computational complexity,different iteration times are established to avoid invalid operation,or to obtain the sub-optimal solution.Through the simulation experiments of the above improvement strategy,the result shows that the improved genetic algorithm obtains faster execution speed compared with the traditional allocation scheme,and the computing resources on each server are more reasonable and balanced,and the expected effect is achieved.