QoS oriented task scheduling based on genetic algorithm in cloud computing

摘要

Task scheduling is one of the most critical issues on cloud platform. The number of users is huge and data volume is tremendous. Requests of resource sharing and reuse become more and more imperative. It's a key issue that how to efficiently and reasonably dispatch users' tasks to different resources according to the Quality of Service(QoS) requirements of both cloud computing centers and users. In order to solve this problem, considering the new characteristics of cloud computing and original Adaptive Genetic Algorithm(AGA), a new scheduling algorithm based on double-fitness adaptive algorithm-job spanning time and user spending(QoS-GA) is proposed. This strategy not only works out a task scheduling sequence with shorter job spanning and average job computing time, but also satisfies subscriber's economic ability. At the same time, this paper adopts greedy algorithm to initialize the population, and weights multi-fitness function. Finally, we compare the performance of QoS-GA with AGA through simulations. It proves the validity of the scheduling algorithm and the effectiveness of the optimization method.