Avoiding Flow Size Overestimation in the Count-Min Sketch with Bloom Filter Constructions

The Count-Min sketch is the most popular data structure for flow size estimation, a basic measurement task required in many networks. Typically the number of potential flows is large, eliminating the possibility to maintain a counter per flow within memory of high access rate. The Count-Min sketch is probabilistic and relies on mapping each flow to multiple counters through hashing. This implies potential estimation error such that the size of a flow is overestimated when all flow counters are shared with other flows with observed traffic. Although the error in the estimation can be probabilistically bounded, many applications can benefit from accurate flow size estimation and the guarantee to completely avoid overestimation. We describe a design of the Count-Min sketch with accurate estimations whenever the number of flows with observed traffic follows a known bound, regardless of the identity of these particular flows. We make use of a concept of Bloom filters that avoid false positives and indicate the limitations of existing Bloom filter designs towards accurate size estimation. We suggest new Bloom filter constructions that allow scalability with the support for a larger number of flows and explain how these can imply the unique guarantee of accurate flow size estimation in the well known Count-Min sketch.Ori Rottenstreich was partially supported by the German-Israeli Foundation for Scientic Research and Development (GIF), by the Gordon Fund for System Engineering as well as by the Technion Hiroshi Fujiwara Cyber Security Research Center and the Israel National Cyber Directorate. Pedro Reviriego would like to acknowledge the sup-port of the ACHILLES project PID2019-104207RB-I00 and the Go2Edge network RED2018-102585-T funded by the Spanish Ministry of Science and Innovation and of the Madrid Community research project TAPIR-CM grant no. P2018/TCS-4496

Distributed Collaborative Monitoring in Software Defined Networks

We propose a Distributed and Collaborative Monitoring system, DCM, with the following properties. First, DCM allow switches to collaboratively achieve flow monitoring tasks and balance measurement load. Second, DCM is able to perform per-flow monitoring, by which different groups of flows are monitored using different actions. Third, DCM is a memory-efficient solution for switch data plane and guarantees system scalability. DCM uses a novel two-stage Bloom filters to represent monitoring rules using small memory space. It utilizes the centralized SDN control to install, update, and reconstruct the two-stage Bloom filters in the switch data plane. We study how DCM performs two representative monitoring tasks, namely flow size counting and packet sampling, and evaluate its performance. Experiments using real data center and ISP traffic data on real network topologies show that DCM achieves highest measurement accuracy among existing solutions given the same memory budget of switches

Optimal Elephant Flow Detection

Monitoring the traffic volumes of elephant flows, including the total byte count per flow, is a fundamental capability for online network measurements. We present an asymptotically optimal algorithm for solving this problem in terms of both space and time complexity. This improves on previous approaches, which can only count the number of packets in constant time. We evaluate our work on real packet traces, demonstrating an up to X2.5 speedup compared to the best alternative.Comment: Accepted to IEEE INFOCOM 201