Abstract: The traditional algorithm has poor performance and its optimization solution cannot meet the diversity needs of users,when deals with the task scheduling in the cloud environment.Based on three optimization goals:task completion time,completion cost,and resource idle rate,this paper simulates the process of heuristic algorithm (the initialization,fitness assessment,task scheduling and selection stages) to construct a hierarchical evaluation and dynamic selection model(HEDSM).In the initialization phase,in order to ensure that tasks have a certain priority,the workflow task model is preprocessed using the traditional table scheduling algorithm (HEFT).In the fitness assessment phase,in order to meet the need of two aspects,the difficult solution evaluation modelsare constructed from two levels which are cloud users and cloud service providers.In the task scheduling phase,two-step scheduling is set.First,the policy set is setting,the task is pre-scheduled to ensure that the pre-scheduling scheme inherits the scheduling advantages of each strategy.Second,in order to enhance the performance of the algorithm,the task migration policy is setting to process the pre-scheduling plan.In the selection phase,the appropriate scheduling scheme is selected in the solution set according to the evaluation modle.The experiment uses WorkflowSim simulation platform and scientific workflow instance to make comparative analysis.Traditional Min-Min,Max-Min,FCFS scheduling strategies and existing IMax-Min and LWRound_Robin scheduling strategies are as comparison algorthms.The algorithms are evaluated from the diversity of user requirements and the IROS two aspects.The results show that the propsed algorithm improves the complete time and cost,therefore it is more suitable for the complex task scheduling in cloud environment.

Key words: Cloud computing, Multi-objective optimization, Task immigration, Task scheduling, Workflow