Adapting to Access Locality via Live Data Migration in Globally Distributed Datastores

Storing data close to where it is used improves the performance of cloud applications. However, data access patterns change dynamically over time. Many datastores statically shard data making locality-adaptation difficult, and some provide limited capability for controlling the data-placement or migration. This leads to increased latency, reduced throughput, and expensive operations. To address this problem, we investigate the requirements for live data-migration and design four data-migration polices. Our policies use heuristics to determine the optimal data placement based on the access locality in the workload and load-balancing constraints. We show that even simple heuristics can be effective, and the topology-aware policies demonstrate overall better results with up to 70% latency improvement in medium locality workloads and nearly 95% improvement in workloads exhibiting very strong single-region access locality.