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 replication as it does not efficiently handle large requests. MALB combine...

• Type: Conference
• Year: 2007
• Where: EUROSYS
• Authors: Sameh Elnikety, Steven G. Dropsho, Willy Zwaenepoel
• Keywords: EUROSYS 2007, Load Balancing Techniques, System Software, Update Filtering, Update Filtering Techniques