Two Samples are Enough: Opportunistic Flow-level Latency Estimation using NetFlow
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The inherent support in routers (SNMP counters or NetFlow) is not sufficient to diagnose performance problems in IP networks, especially for flow-specific problems where the aggregate behavior within a router appears normal. To address this problem, in this paper, we propose a Consistent NetFlow (CNF) architecture for measuring per-flow performance measurements within routers. CNF utilizes existing NetFlow architecture that already reports the first and last timestamps per-flow, and proposes hash-based sampling to ensure that two adjacent routers record the same flows. We devise a novel Multiflow estimator that approximates the intermediate delay samples from other background flows to improve the per-flow latency estimates significantly compared to the naive estimator that only uses actual flow samples. In our experiments using real backbone traces and realistic delay models, we show that the Multiflow estimator is accurate with a median relative error of less than 20% for flows of size greater than 100 packets. We also show that prior approach based on trajectory sampling performs about 2-3x worse. 2010 IEEE.