Elasticat: A load rebalancing framework for cloud-based key-value stores

The problem of load rebalancing is an important issue for cloud-based key-value stores. However, the new virtualization environment and the store's stateful feature make this classical issue more challenging. In this paper, we build a new load rebalancing framework for cloud-based key-value stores, namely ElastiCat. It can be used for auto reconfiguring the store system with minimal costs and no disruption to the availability of the service. To evaluate and minimize the rebalancing costs, we firstly build two interference-aware prediction models to predict the data migration time and performance impact for each action using statistical machine learning and then create a cost model to strike a right balance between them. A cost-aware rebalancing algorithm is designed to utilize the cost model and balance rate to create a rebalancing plan and guide the choice of possible rebalancing actions. To maintain the availability of storage service, we propose a lightweight piggy-back based data access protocol. Finally, we demonstrate the effectiveness of the framework as well as the cost model using YCSB.