Tashkent+: memory-aware load balancing and update filtering in replicated databases

We present a memory-aware load balancing (MALB) technique to dispatch transactions to replicas in a replicated database. Our MALB algorithm exploits knowledge of the working sets of transactions to assign them to replicas in such a way that they execute in main memory, thereby reducing disk I/O. In support of MALB, we introduce a method to estimate the size and the contents of transaction working sets. We also present an optimization called update filtering that reduces the overhead of update propagation between replicas.

We show that MALB greatly improves performance over other load balancing techniques -- such as round robin, least connections, and locality-aware request distribution (LARD) -- that do not use explicit information on how transactions use memory. In particular, LARD demonstrates good performance for read-only static content Web workloads, but it gives performance inferior to MALB for database workloads as it does not efficiently handle large requests. MALB combined with update filtering further boosts performance over LARD.

We build a prototype replicated system, called Tashkent+, with which we demonstrate that MALB and update filtering techniques improve performance of the TPC-W and RUBiS benchmarks. In particular, in a 16-replica cluster and using the ordering mix of TPC-W, MALB doubles the throughput over least connections and improves throughput 52% over LARD. MALB with update filtering further improves throughput to triple that of least connections and more than double that of LARD. Our techniques exhibit super-linear speedup; the throughput of the 16-replica cluster is 37 times the peak throughput of a standalone database due to better use of the cluster's memory.