Database systems serving cloud platforms must serve large numbers of applications (or tenants). In addition to managing tenants with small data footprints, different schemas, and variable load patterns, such multitenant data platforms must minimize their operating costs by efficient resource sharing. When deployed over a pay-per-use infrastructure, elastic scaling and load balancing, enabled by low cost live migration of tenant databases, is critical to tolerate load variations while minimizing operating cost. However, existing databases-relational databases and Key-Value stores alike-lack low cost live migration techniques, thus resulting in heavy performance impact during elastic scaling. We present Albatross, a technique for live migration in a multitenant database serving OLTP style workloads where the persistent database image is stored in a network attached storage. Albatross migrates the database cache and the state of active transactions to ensure minimal impact on transaction execution while allowing transactions active during migration to continue execution. It also guarantees serializability while ensuring correctness during failures. Our evaluation using two OLTP benchmarks shows that Albatross can migrate a live tenant database with no aborted transactions, negligible impact on transaction latency and throughput both during and after migration, and an unavailability window as low as 300 ms.
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Computer Science(all)