Data Transformation in Cloud for Effective Cost Optimization

Abstract

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. We further develop a cost model guided planner to efficiently find the optimized transformation for a predefined goal and our experimental results demonstrate the effectiveness of ToF in optimizing the performance and cost in comparison with other existing approaches. Cloud computing offers its customers an economical and convenient pay-as-you- go service model, known also as usage based pricing. Cloud customers pay only for the actual use of computing resources, storage, and band-width, according to their changing needs, utilizing the cloud’s scalable and elastic computational capabilities. In particular, data transfer costs (i.e., bandwidth) is an important issue when trying to minimize costs.