Clustering Coefficient-Based Workflow Slicing and Multi-Cloud Scheduling

Abstract:

Workflow scheduling in multi-cloud environment is a research hotspot and challenge in recent years. The dependencies in workflow are usually represented by the transmission of data， which also determines the execution order of tasks. Existing studies for workflow scheduling usually map each task to a different cloud resource， which is difficult to solve the problems of increasing make-span and cost， and the possible failure risk caused by frequent data communication. In order to reduce the impact of data communication between tasks， this paper proposes a workflow slicing and multi-cloud scheduling solution based on clustering coefficient. Preliminary slicing of workflow is conducted by using a clustering algorithm， and the clustering coefficient is introduced to evaluate and optimize the slicing effect. In the process of finding the optimal scheduling solution， the slicing result is adjusted dynamically according to the actual situation of cloud instances. Experimental results show that the proposed method can effectively reduce the high cost and make-span caused by large amount of data communications in workflow.