Correlation-Aware Flow Consolidation for Load Balancing and Beyond

Abstract

Existing load balancing solutions rely on direct or indirect measurement of rates (or congestion) averaged over short periods of time. Sudden fluctuations in flow rates can lead to significant undershooting/ overshooting of target link loads. In this paper, we make the case for taking variations and correlations of flows into account in load balancing. We propose correlation-aware flow consolidation, i.e. aggregating inversely correlated (or uncorrelated) flows into superflows and using them as building blocks for load balancing. Superflows are smoother than individual flows, and thus are easier to estimate with a higher confidence, and can reduce overshooting/ undershooting of link capacities. We present heuristic methods combined with predictive models to consolidate flows and show they can lead to significant reductions in rate standard deviations compared to correlation-agnostic solutions (up to 33% and 12% improvements at the 50th and 99th percentiles respectively for 20 superflows based on real traffic traces).