Research On Task Scheduling Of Genetic Algorithm Optimization In Cloud Environment

Cloud computing is a new type of computing model that has developed rapidly in recent years.It first pools resources such as software and hardware through the network to form a "resource pool",and then provides various basic services to the world through the network.Users save a lot of money,and at the same time create more convenient conditions for the development of some emerging industries.However,since cloud platforms need to face a large number of data and computing tasks,how to allocate resources in the cloud environment rationally and design an efficient task scheduling strategy to minimize costs,improve execution efficiency,and meet user needs are the moment The key problem that cloud computing needs to solve.The research content of this article is as follows:In the cloud environment,genetic algorithm task scheduling has problems of poor optimization ability and unstable results.For the above problems,a genetic algorithm based on variance and directed mutation(Variance-Directional Variation Genetic Algorithm,V-DVGA)is proposed.In the selection part,multiple selections are made during each iteration.Combined with the idea of mathematical variance,the selected population is retained,which enriches the variety of individuals in the population and expands the search range for better solutions.Introduce linear weight distribution strategy in fitness evaluation function modeling to enhance the flexibility of evaluation criteria.In the intersection part,establish a new intersection mechanism,calculate the intersection probability according to the fitness function value of the individual,enrich the diversity of the population and improve the overall fitness of the population.In the mutation part,establish a mutation mechanism,use directional mutation on the basis of traditional mutation,and control the quality of the individual after mutation to improve the optimization ability of the algorithm.Through the experimental comparison of the workflowsim simulation platform,the analysis of the experimental results shows that: under the condition of ensuring that the cost does not increase,the optimization ability and execution efficiency of the algorithm are improved,and the problem that the traditional genetic algorithm results are unstable is improved.