Research On Task Scheduling Based On Double-fitness Genetic Algorithm In Cloud Environment

Cloud computing is a new computing model after distributed computing, grid computing. It provides services to users in the form of computing, storage, and platform. Cloud computing develops rapidly driven by cloud giants in and abroad and present a good solution for big data. Google put forward MapReduce concurrent processing mass data programming model in2004, cloud platform based on MapReduce model constantly emerging, of which the most widely is Hadoop. Task scheduling based on cloud model has become the hot issue of researches. Efficient task scheduling algorithm can improve the overall performance of the cloud computing system and more reasonable allocate the resources in cloud computing center, which plays an important part in cloud computing platform with mass data processing.Task scheduling is one of the most critical issues on cloud platform. The number of users is huge and data volume is tremendous. How to schedule tasks efficiently is an important issue to be resolved in cloud computing. 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-Total job spanning time and Average job spanning time genetic algorithm (TAGA) is established. This strategy not only works out a tasks scheduling sequence with shorter job spanning, but also minimizes average job make span.We then compare the performance of TAGA with AGA through Matlab simulations. It proves the validity of the scheduling algorithm and the effectiveness of the optimization method both in total and average job spanning.