TOF-A GENERAL TRANSFORMATION-BASED OPTIMIZATION FRAMEWORK FOR WORKFLOWS IN THE CLOUD

A workflow management system should balance the cost and performance. Thus, performance and (monetary) cost optimizations have recently become a hot research topic for workflows in the cloud. However, we find that most existing studies adopt ad hoc optimization strategies, which fail to capture the key optimization opportunities for different workloads and cloud offerings (e.g., virtual machines with different prices). First, we propose a transformation-based workflow optimization system to address the performance and monetary cost optimizations in the cloud. Second, we develop and deploy the workflow optimization system in real cloud environments, and demonstrate its effectiveness and efficiency with extensive experiments. To the best of our knowledge, this work is the first of its kind in developing a general optimization engine for minimizing the monetary cost of running workflows in the cloud.