PROJECT TITLE :
Exploiting Order Independence for Scalable and Expressive Packet Classification
Efficient packet classification could be a core concern for network services. Ancient multi-field classification approaches, in both software and ternary content-addressable memory (TCAMs), entail tradeoffs between (memory) space and (lookup) time. TCAMs cannot efficiently represent range rules, a typical class of classification rules confining values of packet fields to given ranges. The exponential space growth of TCAM entries relative to the number of fields is exacerbated when multiple fields contain ranges. During this work, we tend to gift a unique approach which identifies properties of many classifiers that will be implemented in linear house and with worst-case guaranteed logarithmic time and allows the addition of a lot of fields as well as range constraints while not impacting house and time complexities. On real-life classifiers from Cisco Systems and additional classifiers from ClassBench (with real parameters), 90–95p.c of rules are so handled, and the other 5–ten% of rules can be stored in TCAM to be processed in parallel.
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