Achieving performance consistency in heterogeneous clusters

Hash-based randomization is a powerful technique used in clusters and distributed systems for load management. It offers uniform distribution, efficient addressing, little shared state, and scalability. However, simple hash-based randomization is unable to deal with skew and heterogeneity and, therefore, cannot achieve load balance in many environments. Virtual processors have been proposed as a solution to simple randomization's problem. We evaluate an alternative load management scheme for heterogeneous, shared-disk clusters. Our scheme directly tunes hash-based randomized load placement using a technique called adaptive, nonuniform (ANU) randomization [2003] and compares favorably to the virtual processor approach. It provides the load balancing benefits of virtual processors with less shared state. It also automatically adapts to workload and cluster configuration changes, such as failure and recovery and adding or removing servers, without human involvement. Experimental results show that our scheme outperforms virtual processors and performs comparably to prescient load-balancing algorithms. They also show that our system maintains consistent performance across all servers while moving a minimal amount of load.