Data Aggregators For Supporting Cloud Data Migration

Abstract

More and more enterprises and organizations are hosting their data into the cloud, in order to reduce the IT maintenance cost and enhance the data reliability. However, facing the numerous cloud vendors as well as their heterogeneous pricing policies, customers may well be perplexed with which cloud(s) are suitable for storing their data and what hosting strategy is cheaper. The general status quo is that customers usually put their data into a single cloud (which is subject to the vendor lock-in risk) and then simply trust to luck. Based on comprehensive analysis of various state-of-the-art cloud vendors, this data hosting integrates two key functions desired. The first is selecting several suitable clouds and an appropriate redundancy strategy to store data with minimized monetary cost and guaranteed availability. The second is triggering a transition process to re-distribute data according to the variations of data access pattern and pricing of clouds. In this I evaluate the performance of this using both trace-driven simulations and prototype experiments. The results show that compared with the major existing schemes this not only saves around 20% of monetary cost but also exhibits sound adaptability to data and price adjustments.