Optimizing data migration for cloud-based key-value stores

Abstract

As one database offloading strategy, elastic key-value stores are often introduced to speed up the application performance with dynamic scalability. Since the workload is varied, efficient data migration with minimal impact in service is critical for the issue of elasticity and scalability. However, due to the new virtualization technology, real-time and low-latency requirements, data migration within cloud-based key-value stores has to face new challenges: effects of VM interference, and the need to trade off between the two ingredients of migration cost, namely migration time and performance impact. To fulfill these challenges, in this paper we explore a new approach to optimize the data migration. Explicitly, we build two interference-aware models to predict the migration time and performance impact for each migration action using statistical machine learning, and then create a cost model to strike a balance between the two ingredients. Using the load rebalancing scenario as a case study, we have designed one cost-aware migration algorithm that utilizes the cost model to guide the choice of possible migration actions. Finally, we demonstrate the effectiveness of the approach using Yahoo! Cloud Serving Benchmark (YCSB).