A Fast and Compact Method for Unveiling Significant Patterns in High Speed Networks

Abstract

Identification of significant patterns in network traffic, such as IPs or flows that contribute large volume (heavy hitters) or introduce large changes (heavy changers), has many applications in accounting and network anomaly detection. As network speed and the number of flows grow rapidly, tracking per-IP or per-flow statistics becomes infeasible due to both the computational overhead and memory requirements. In this paper, we propose a novel sequential hashing scheme that requires only O(H log N) both in memory and computational overhead that are close to being optimal, where N is the the number of all possible keys (e.g., flows, IPs) and H is the maximum number of heavy keys. Moreover, the generalized sequential hashing scheme makes it possible to trade off among memory, update cost, and detection cost in a large range that can be utilized by different computer architectures for optimizing the overall performance. In addition, we also propose statistically efficient algorithms for estimating the values of heavy hitters and heavy changers. Using both theoretical analysis and experimental studies of Internet traces, we demonstrate that our approach can achieve the same accuracy as the existing methods do but using much less memory and computational overhead.