Probabilistic lossy counting: an efficient algorithm for finding heavy hitters

Knowledge of the largest traffic flows in a network is important for many network management applications. The problem of finding these flows is known as the heavy-hitter problem and has been the subject of many studies in the past years. One of the most efficient and well-known algorithms for finding heavy hitters is lossy counting [29]. In this work we introduce probabilistic lossy counting (PLC), which enhances lossy counting in computing network traffic heavy hitters. PLC uses on a tighter error bound on the estimated sizes of traffic flows and provides probabilistic rather than deterministic guarantees on its accuracy. The probabilistic-based error bound substantially improves the memory consumption of the algorithm. In addition, PLC reduces the rate of false positives of lossy counting and achieves a low estimation error, although slightly higher than that of lossy counting. We compare PLC with state-of-the-art algorithms for finding heavy hitters. Our experiments using real tra...

• Type: Journal
• Year: 2008
• Where: CCR
• Authors: Xenofontas A. Dimitropoulos, Paul Hurley, Andreas Kind
• Keywords: CCR 2008, Heavy Hitters, Lossy Counting, Traffic Heavy Hitters