iCostale: Adaptive cost optimization for storage clouds

Abstract

The unprecedented volume of data generated by contemporary business users and consumers has created enormous data storage and management challenges. In order to control data storage cost, many users are moving their data to online storage clouds, and applying capacity usage reducing data transformation techniques like de-duplication, compression, and transcoding. These give rise to several challenges, such as which cloud to choose, and what data transformation techniques to apply for optimizing cost. This paper presents an integrated storage service called iCostale that reduces the overall cost of data storage through automatic selection and placement of users data into one of many storage clouds. Further, it intelligently transforms data based on its type, access frequency, transformation overhead, and the cost model of the storage cloud providers. We demonstrate the efficacy of iCostale through a series of micro- and application-level benchmarks. Our experimental results show that, through intelligent data placement and transformation, iCostale can reduce overall cost of data storage by more than 50%. © 2011 IEEE.