The Research of Scheduling Algorithm Faced to the Matching Degree of User's Task and Cloud Resource

To improve the execution efficiency of task in cloud environment, the Matching function of Task Resource and Cost Advanced Ant Colony Optimization(MTRCACO)is proposed.The MTRCACO improves inspiration information from information elements by making a comprehensive reference of the latest ant colony algorithms and adopting the matching function of task and resource.It also decreases the load unbalancing degree in cloud computing center by cost function, and simultaneously keeps the load balance of virtual machines by executing the tasks for many times.Some experiments are carried out on the CloudSim platform.The comparison shows this MTRCACO algorithm is more efficient than the algorithms IPSO and BACO in reducing task execution cost and in keeping system load balance, thus optimizes resource utilization.