Packet classification in large ISPs

Abstract

Packet classification, although extensively studied, is an evolving problem. Growing and changing needs necessitate the use of larger filters with more complex rules. The increased complexity and size pose implementation challenges on current hardware solutions and drive the development of software classifiers, in particular, decision-tree based classifiers. Important performance measures for these classifiers are time and memory due to required high throughput and use of limited fast memory.We analyze Tier 1 ISP data that includes filters and corresponding traffic from over a hundred edge routers and thousands of interfaces. We provide a comprehensive view on packet classification in an operational network and glean insights that help us design more effective classification algorithms.We propose and evaluate decision tree classifiers with common branches . These classifiers have linear worst-case memory bounds and require much less memory than standard decision tree classifiers, but nonetheless, we show that on our data have similar average and worst-case time performance. We argue that common-branches exploit structure that is present in real-life data sets.We observe a strong Zipf-like pattern in the usage of rules in a classifier, where a very small number of rules resolves the bulk of traffic and most rules are essentially never used. Inspired by this observation, we propose traffic-aware classifiers that obtain superior average-case and bounded worst-case performance. Good average-case can boost performance of software classifiers that can be used in small to medium sized routers and are also important for traffic analysis and traffic engineering.