To move or not to move: Cost optimization in a dual cloud-based storage architecture

IT enterprises have recently witnessed a dramatic increase in data volume and faced with challenges of storing and retrieving their data. Thanks to the fact that cloud infrastructures offer storage and network resources in several geographically dispersed data centers (DCs), data can be stored and shared in scalable and highly available manner with little or no capital investment. Due to diversity of pricing options and variety of storage and network resources offered by cloud providers, enterprises encounter nontrivial choice of what combination of storage options should be used in order to minimize the monetary cost of managing data in large volumes. To minimize the cost of data storage management in the cloud, we propose two data object placement algorithms, one optimal and another near optimal, that minimize residential (i.e., storage, data access operations), delay, and potential migration costs in a dual cloud-based storage architecture (i.e., the combination of a temporal and a backup DC). We evaluate our algorithms using real-world traces from Twitter. Results confirm the importance and effectiveness of the proposed algorithms and highlight the benefits of leveraging pricing differences and data migration across cloud storage providers (CSPs). & 2016 Elsevier Ltd. All rights reserved.