Adaptive and flexible key-value stores through soft data partitioning

Abstract

Key-value stores such as Memcached have become widely used by cloud and web-service providers. While there has been a significant amount of research done on improving the absolute performance of key-value stores, this work proposes an adaptive and a flexible approach to key-value stores. We first propose soft data partitioning that divides memory into multiple groups within a single node, or a single server process, to enable scale-up of key-value stores, while providing NUMA locality and an adaptive approach that can reduce overall request miss rate. The soft-partitioning enables a flexible Memcached server implementation in a NUMA system through NUMA-aware allocation as well as power-efficient NUMA server operation by migrating frequently accessed key-value pairs among the groups. We also propose an adaptive replacement policy within Memcached server that compares miss rates across the different memory groups to determine a more optimal replacement policy. To overcome the limitation of partitioning, we propose Group Auto-Balancing (GAB) where memory allocation from the different groups can be borrowed to minimize miss rate. Our results improve Memcached throughput by 12.9%, on average, over previously proposed MemC3 algorithm (up to 3.1× for write intensive workloads) while the adaptive replacement policy shows the lowest miss rate on adversarial access patterns.