Automated Learning of Workload Measures for Load Balancing on a Distributed System

Load-balancing systems use workload indices to dynamically schedule jobs. We present a novel method of automatically learning such indices. Our approach uses comparator neural networks, one per site, which learn to predict the relative speedup of an incoming job using only the resource-utilization patterns observed prior to the job´s arrival. Our load indices combine information from the key resources of contention: CPU, disk, network, and memory.