A Cost-Optimal Service Selection Approach for Collaborative Workflow Execution in Clouds

Today, there has been a strong demand of distributed collaboration in design and manufacturing, due to the acceleration of economic globalization and the popularity of virtual enterprises (VE) model. Because of the characteristics of cloud computing, such as elasticity and on-demand computing, it is promising to deploy and execute collaborative workflows that contain multiple tasks and services such as Computer-Aided Design (CAD) software components on cloud resources for supporting collaboration across enterprises. Specifically, how to cost-effectively select appropriate services to execute workflows within deadlines while without violating multiple constraints becomes an important issue. In this paper, through investigating the practical requirements of collaborative design workflow, we first formulate the issue of the cost-optimal cloud service selection for collaborative workflow executions as a multidimensional optimization problem with multiple constraints. Then we propose an effective approach based on genetic algorithms to address this problem for obtaining near-optimal solutions. Based on workload data derived from real-world systems, we conduct experiments which show that our approach outperforms traditional greedy algorithms in finding better solutions and it also provides real-time performance guarantees in real-world cloud computing environments.