A Framework for Efficient Class-Based Scheduling

With an increasing requirement for network monitoring tools to classify traffic and track security threats, newer and efficient ways are needed for collecting traffic statistics and monitoring of network flows. However, traditional solutions based on random packet sampling treat all flows as equal and therefore, do not provide the flexibility required for these applications. In this paper, we propose a novel architecture called CLAMP that provides an efficient framework to implement size-based sampling. At the heart of CLAMP is a novel data structure called composite bloom filter (CBF) that consists of a set of bloom filters that work together to encapsulate various class definitions. In comparison to previous approaches that implement simple size-based sampling, our architecture requires substantially lower memory (upto 80x) and results in higher flow coverage (upto 8x more flows) under specific configurations.