|M.Sc Student||Nezri Yuval|
|Subject||Cardinality Estimation In a Virtualized Network Device Using|
Online Machine Learning
|Department||Department of Computer Science||Supervisor||Professor Reuven Cohen|
Cardinality estimation algorithms receive a stream of elements, with possible repetitions, and return the number of distinct elements in the stream. Such algorithms seek to minimize the required memory and CPU resource consumption at the price of inaccuracy in their output. In computer networks, cardinality estimation algorithms are mainly used for counting the number of distinct flows in an IP packet stream. This is important for tracking the load imposed on a web server, detecting a potential Distributed Denial of Service (DDoS) attack, and discovering other network anomalies. Cardinality estimation algorithms are divided into two categories: sketching algorithms and sampling algorithms. Sketching algorithms require the processing of all packets, and they are therefore usually implemented by dedicated hardware. Sampling algorithms do not require processing of all packets, but they are known for their inaccuracy.
Recently, software network devices are gaining popularity due to the rise of two new network architecture concepts: Software Defined Networking (SDN) and Network Function Virtualization (NFV). The transition from executing cardinality estimation algorithms by dedicated hardware to executing them by a general purpose CPU introduces new challenges. Even with a very small number of operations per packet, processing every stream packet by a general purpose CPU is inefficient and in many cases even impossible. Because it is usually impossible to process all the packets, sketching cannot be used. In such cases we must resort to sampling-based solutions.
In this work we identify one of the major drawbacks of sampling-based cardinality estimation algorithms: their inability to adapt to changes in flow size distribution. To address this problem, we propose a new sampling-based adaptive cardinality estimation framework, which uses online machine learning. We describe the framework and its parameters, and then discuss the selection of an online ML algorithm and its features. We evaluate our framework using real traffic traces, and show significantly better accuracy compared to the best known sampling-based algorithms, for the same fraction of processed packets.