Author_Institution :
Inf. Eng. Inst., Henan Radio & TV Univ., Zhengzhou, China
Abstract :
According to the traditional mobile learning terminal in the cloud computing environment, the multi tenant scheduling mode adopts the static resource scheduling method under the mechanism of allocation in advance, and it needs many preconditions. The allocation of resources is taken according to the preconditions. However, we cannot get the preconditions for all the states, and it will produce serious waste of resource in the process of allocation, the scheduling efficiency is greatly reduced. In order to solve the problem, an optimal software scheduling method is proposed for multi-tenant based on repeated game algorithm in cloud computing environment. According to the simulated annealing theory, the initial population of multi-tenant software scheduling is obtained, and the corresponding adaptive function is computed. The selection, crossover and mutation operations are carried out for the population. The simulated annealing results are obtained, and the new species are produced. According to the repeated game theory, the objective function of multi-tenant software scheduling is obtained. The tenant software data is taken with initialization processing. The tenants are updated, and the software scheduling for multi-tenant in cloud computing environment is realized. Simulation result shows that the improved algorithm can be applied in software scheduling of multi-tenant in cloud computing environment, and the efficiency of scheduling is improved greatly.
Keywords :
cloud computing; computer aided instruction; digital simulation; game theory; mobile computing; resource allocation; scheduling; simulated annealing; cloud computing; crossover operations; mobile learning terminal; multitenant software scheduling; mutation operations; optimal scheduling simulation; optimal software scheduling method; preconditions; repeated game algorithm; resource allocation; scheduling efficiency reduction; selection operations; simulated annealing theory; software simulation; static resource scheduling method; tenant software data; Cloud computing; Processor scheduling; Scheduling; Sociology; Software algorithms; Statistics; cloud computing; mobile learning; multi-tenant software; resource scheduling;