Prediction and adaptation in Active Harmony
Cluster Computing
View Divergence Control of Replicated Data Using Update Delay Estimation
SRDS '99 Proceedings of the 18th IEEE Symposium on Reliable Distributed Systems
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We introduce a new performance metric, called Load Balancing Factor (LBF), to assist programmers with evaluating different tuning alternatives. The LBF metric differs from traditional performance metrics since it is intended to measure the performance implications of a specific tuning alternative rather than quantifying where time is spent in the current version of the program. A second unique aspect of the metric is that it provides guidance about moving work within a distributed or parallel program rather than reducing it. A variation of the LBF metric can also be used to predict the performance impact of changing the underlying network. The LBF metric can be computed incrementally and online during the execution of the program to be tuned. We also present a case study that shows that our metric can predict the actual performance gains accurately for a test suite of six programs.