Lili Chen ^* Yuxia Niu

• Nantong College of Science and Technology, Nantong, China

In cloud computing, a common idea to reduce operation cost and improve service quality is to study task scheduling algorithms. To better allocate virtual machine resources, a virtual machine resource scheduling algorithm, Shapley Value Method-Genetic Algorithm, (SVM-GA) is proposed. This algorithm uses SVM to obtain the contribution values of each component of the virtual machine, refine the topological network, and achieve the optimal solution of scheduling by genetic algorithm. CloudSim simulation results show that SVM-GA has the lowest total task completion time when comparing with existing intelligent optimization algorithms (such as max-min algorithm, logistic regression algorithm, and differential evolution algorithm) with the same number of tasks, and the total task time is 25s, 55s, 81s ,112s, 145s, and 175s for 200, 400, 600, 800, 1000, and 1200 tasks, respectively. As the number of evolutionary generations increases, the ability of SVM-GA to solve the optimal solution of the model increases. In the simulated light load case, the SVM-GA migration time and migration count optimal solutions are slightly inferior to the logistic regression algorithm (3.02s > 2.38s; 1129 times > 999 times), but the migration energy consumption and service level agreement violation rate optimal solutions are the best. The SVM-GA performance in the heavy load case is similar to the light load case. The experiments show the feasibility of the algorithm proposed in the study.