an identification method combining data streaming counting with probabilistic fading for heavy-hitter flows

Identifying heavy-hitter flows in the network is of tremendous importance for many network management activities. Heavy-hitter flows identification is essential for network monitoring, management, and charging, etc. Network administrators usually pay special attention to these Heavy-hitter flows. How to find these flows has been the concern of many studies in the past few years. Lossy counting and probabilistic lossy counting are among the most well-known algorithms for finding Heavy-hitters. But they have some limitations. The challenge is finding a way to reduce the memory consumption effectively while achieving better accuracy. In this work, a probabilistic fading method combining data streaming counting is proposed, which is called PFC(probabilistic fading counting). This method leverages the advantages of data streaming counting, and it manages to find the heavy-hitter by analyzing the power-low characteristic in the network flow. By using network's power-law and continuity, PFC accelerates the removal of non-active and aging flows in table records. So PFC reduces memory consumption, and decreases false positive ratio too. Comparisons with lossy counting and probabilistic lossy counting based on real Internet traces suggest that PFC is remarkably efficient and more accurate. Particularly, experiment results show that PFC has 60% lower memory consumption without increasing the false positive ratio.Identifying heavy-hitter flows in the network is of tremendous importance for many network management activities. Heavy-hitter flows identification is essential for network monitoring, management, and charging, etc. Network administrators usually pay special attention to these Heavy-hitter flows. How to find these flows has been the concern of many studies in the past few years. Lossy counting and probabilistic lossy counting are among the most well-known algorithms for finding Heavy-hitters. But they have some limitations. The challenge is finding a way to reduce the memory consumption effectively while achieving better accuracy. In this work, a probabilistic fading method combining data streaming counting is proposed, which is called PFC(probabilistic fading counting). This method leverages the advantages of data streaming counting, and it manages to find the heavy-hitter by analyzing the power-low characteristic in the network flow. By using network's power-law and continuity, PFC accelerates the removal of non-active and aging flows in table records. So PFC reduces memory consumption, and decreases false positive ratio too. Comparisons with lossy counting and probabilistic lossy counting based on real Internet traces suggest that PFC is remarkably efficient and more accurate. Particularly, experiment results show that PFC has 60% lower memory consumption without increasing the false positive ratio