Tunable randomization for load management in shared-disk clusters

We develop and evaluate a system for load management in shared-disk file systems built on clusters of heterogeneous computers. It balances workload by moving file sets among cluster server nodes. It responds to changing server resources that arise from failure and recovery, and dynamically adding or removing servers. It also realizes performance consistency---nearly uniform performance across all servers. The system is adaptive and self-tuning. It operates without any a priori knowledge of workload properties, or the capabilities of the servers. Rather, it continuously tunes load placement using a technique called adaptive, nonuniform (ANU) randomization. ANU randomization realizes the scalability and metadata reduction benefits of hash-based, randomized placement techniques, while avoiding hashing's drawbacks: load skew, inability to cope with heterogeneity, and lack of tunability. ANU randomization outperforms virtual-processor approaches to load balancing, while reducing the amount of shared state among servers and the amount of load movement.