A QOS-oriented Hybrid Scheduling Algorithm for Multi-tasks in a Cloud Robot Environment

In the cloud computing environment, how to schedule tasks efficiently according to users' service requirements and improve the quality of service have become a hot research topic. Based on the research of independent task scheduling model and AMM algorithm (Average Minus Minimum), a new PAW(Priority, AMM and Weight)algorithm combined with Priority, AMM and Weight is proposed. Through quantifying task priorities and execution effect on each node, PAW solves many remaining problems in other algorithms, such as long waiting time of urgent tasks, low qualities of services and so on. CloudSim, a cloud computing simulator is used as a tool to analyze PAW algorithm, and the simulation results show that PAW algorithm has good effects on priority processing, load balancing and makespan. This demonstrates that the proposed algorithm is an effective task scheduling method in cloud computing environment.