Transformation-Based Monetary Cost Optimizations for Workflows in the Cloud
Recently, performance and monetary cost optimizations for workflows from various applications in the cloud have become a hot research topic. However, the authors 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). This paper proposes ToF, a general transformation-based optimization framework for workflows in the cloud. Specifically, ToF formulates six basic workflow transformation operations. An arbitrary performance and cost optimization process can be represented as a transformation plan (i.e., a sequence of basic transformation operations). All transformations form a huge optimization space.