An intelligent dynamic load balancer for workstation clusters
ACM SIGOPS Operating Systems Review
Prediction and adaptation in Active Harmony
Cluster Computing
FBLB: a feedback based scheme for scheduling medical post processing applications in clusters
AIC'06 Proceedings of the 6th WSEAS International Conference on Applied Informatics and Communications
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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.