Adaptive Sketch:accurate flow size measurement in high-speed networks
The measurement of flow size in high-speed networks faces a significant challenge due to the scarcity of high-speed memory resources,making it difficult to meet the real-time storage demands of massive flow data.Existing works commonly rely on memory-sharing techniques to place designed estimators in the limited high-speed memory.However,this approach introduces a substantial amount of noise that is hard to eliminate,leading to lower estimation accuracy for medium and small-scale flows.This paper proposes an Adaptive Sketch technique that adapts the memory space based on the flow size to address this issue.Building upon this technique,a high-precision,low-memory-cost flow size estimator is designed.The flow size estimator efficiently filters out massive noising/small flows using reversible counters and further employs sample counters with decreasing sampling probabilities at each level to adaptively sample different-sized flows.This technique effectively controls memory usage by large flows,achieving low cost and high precision in flow size measurement.Experiments based on the real network dataset CAIDA 2019 demonstrate that the proposed flow size estimator reduces the average relative error by nearly 1 order of magnitude compared to existing mechanisms.
NETL
NSTL
万方数据
