Space-Code Bloom Filter for Efficient Per-Flow Traffic Measurement

摘要

Per-flow traffic measurement is critical for usage accounting, traffic engineering, and anomaly detection. Previous methodologies are either based on random sampling (e.g., Cisco's NetFlow), which is inaccurate, or only account for the "elephants." We introduce a novel technique for measuring per-flow traffic approximately, for all flows regardless of their sizes, at very high-speed (say, OC768). The core of this technique is a novel data structure called Space-Code Bloom Filter (SCBF). A SCBF is an approximate representation of a multiset; each element in this multiset is a traffic flow and its multiplicity is the number of packets in the flow. The multiplicity of an element in the multiset represented by SCBF can be estimated through either of two mechanisms-maximum-likelihood estimation or mean value estimation. Through parameter tuning, SCBF allows for graceful tradeoff between measurement accuracy and computational and storage complexity. SCBF also contributes to the foundation of data streaming by introducing a new paradigm called blind streaming. We evaluate the performance of SCBF through mathematical analysis and through experiments on packet traces gathered from a tier-1 ISP backbone. Our results demonstrate that SCBF achieves reasonable measurement accuracy with very low storage and computational complexity. We also demonstrate the application of SCBF in estimating the frequency of keywords at a search engine-demonstrating the applicability of SCBF to other problems that can be reduced to multiset membership queries