The networking industry is facing enormous challenges of scaling devices to support theexponential growth of internet traffic as well as increasing number of features being implemented inside the network. Algorithmic hardware improvements to networking componentshave largely been neglected due to the ease of leveraging increased clock frequency and compute power and the risks of implementing complex hardware designs. As clock frequencyslows its growth, algorithmic solutions become important to fill the gap between currentgeneration capability and next generation requirements. This paper presents algorithmicsolutions to networking problems in three domains: Deep Packet Inspection(DPI), firewall(and other) ruleset compression and non-cryptographic hashing. The improvements in DPIare two-pronged: first in the area of application-level protocol field extraction, which allowssecurity devices to precisely identify packet fields for targeted validity checks. By usingcounting automata, we achieve precise parsing of non-regular protocols with small, constantper-flow memory requirements, extracting at rates of up to 30gbps on real traffic in softwarewhile using only 112 bytes of state per flow. The second DPI improvement is on the longstanding regular expression matching problem, where we complete the HFA solution to theDFA state explosion problem with efficient construction algorithms and optimized memorylayout for hardware or software implementation. These methods construct automata toocomplex to be constructed by previous methods in seconds, while being capable of 29gbpsthroughput with an ASIC implementation. Firewall ruleset compression enables more firewall entries to be stored in a fixed capacity pattern matching engine, and can also be usedto reorganize a firewall specification for higher performance software matching. A novelrecursive structure called TUF is given to unify the best known solutions to this problemand suggest future avenues of attack. These algorithms, with little tuning, achieve a 13.7%improvement in compression on large, real-life classifiers, and can achieve the same results asexisting algorithms while running 20 times faster. Finally, non-cryptographic hash functionscan be used for anything from hash tables to track network flows to packet sampling fortraffic characterization. We give a novel approach to generating hardware hash functionsin between the extremes of expensive cryptographic hash functions and low quality linearhash functions. To evaluate these mid-range hash functions properly, we develop new evaluation methods to better distinguish non-cryptographic hash function quality. The hashfunctions described in this paper achieve low-latency, wide hashing with good avalanche anduniversality properties at a much lower cost than existing solutions.
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