Towards a Cost-Aware Data Migration Approach for Key-Value Stores

Abstract

Live data migration is an important technique for key-value stores. However, due to the stateful feature, new virtualization technology, stringent low latency requirements and unexpected workload changes, key-value stores deployed in cloud environment have to face new challenges for data migration: effects of VM interference, and the need to trade off between the two ingredients of migration cost, say migration time and performance impact. To address these challenges, we focus on the data migration problem in a load rebalancing scenario and build a new framework that aims to rebalance load while minimizing migration costs. We build two interference-aware prediction models to predict the migration time and performance impact for each action using statistical machine learning and then create a cost model to strike a right balance between the two ingredients of cost. A cost-aware migration algorithm is designed to utilize the cost model and balance rate to guide the choice of possible migration actions. We demonstrate the effectiveness of the data migration approach as well as the cost model and two prediction models using YCSB.