A data streaming algorithm for estimating entropies of od flows

Entropy has recently gained considerable significance as an important metric for network measurement. Previous research has shown its utility in clustering traffic and detecting traffic anomalies. While measuring the entropy of the traffic observed at a single point has already been studied, an interesting open problem is to measure the entropy of the traffic between every origin-destination pair. In this paper, we propose the first solution to this challenging problem. Our sketch builds upon and extends the Lp sketch of Indyk with significant additional innovations. We present calculations showing that our data streaming algorithm is feasible for high link speeds using commodity CPU/memory at a reasonable cost. Our algorithm is shown to be very accurate in practice via simulations, using traffic traces collected at a tier-1 ISP backbone link. Categories and Subject Descriptors C.2.3 [Computer-Communication Networks]: Network Operations—Network Monitoring General Terms Algorithms...

• Type: Conference
• Year: 2007
• Where: IMC
• Authors: Haiquan (Chuck) Zhao, Ashwin Lall, Mitsunori Ogihara, Oliver Spatscheck, Jia Wang, Jun Xu
• Keywords: Data Streaming Algorithm, Entropy, IMC 2007, Internet Technology, Network Measurement