۱٫ Introduction

۲٫ System Model

۳٫ Priority Partition and Dynamic Adjustment

۴٫ Multi Workflow Task Fair Allocation Strategy Based on the Reinforcement Learning

۵٫ Experimental Results

۶٫ Conclusion

Acknowledgement

References

In this study, aiming to optimize the multi-workflow scheduling order, in which tasks submitted at different time require different service quality, we present a fair multi-workflow scheduling scheme based on reinforcement learning. Firstly we design a dynamic priority-driven algorithm, in order to set the initial state of the task priority according to the type of cloud workflow and service quality on the one hand, and on the other hand, to adjust the tasks priority dynamically while scheduling so as to avoid violating the Service Level Agreement by delaying the workflow provisioning. Secondly, we design a fine-grained cloud computing model and apply the reinforcement-learning based scheduling algorithm to balance the cluster loads. Finally the experimental results prove the effectiveness of this scheme.

Introduction

The cloud workflow (the scientific workflow, the multi-layer Web service workflow, the MapReduce workflow and the Dryad workflow, etc) is a new application model of workflow in the cloud computing environment[2]. The scheduling problem under the sharing heterogeneous distributed resources (the public cloud, the private cloud or the hybrid cloud, etc) has attracted wide attention from researchers in recent years. Chen et al [3] have designed the dynamic task rearrangement and the scheduling algorithm under the prior constraint in view of the task fairness allocation problem of the multi workflow. Fard et al have designed a multi-objective optimization algorithm in view of the heterogeneous cloud computing environment. The algorithm has divided the global task allocation problem into several sub assignment problems and each sub problem is solved by the meta heuristic algorithm[4]. Jing Weipeng et al [5] have proposed a dynamic multi layered DAG scheduling algorithm[6] that considers the link communication competition between virtual machines in view of the reliable scheduling problem of multiple cloud scientific workflow in the cloud computing environment, which has effectively solved the fair scheduling problem when the weights of the tasks in several DAG are greatly different.