PROJECT TITLE :

Finding Nonequivalent Classifiers in Boolean Space to Reduce TCAM Usage

ABSTRACT:

Packet classification is one in all the foremost challenges today in designing high-speed routers and firewalls, because it involves refined multi-dimensional looking out. Ternary content addressable memory (TCAM) has been widely used to implement packet classification, due to its parallel search capability and constant processing speed. However, TCAMs have limitations of high cost and high power consumption, which ignite the will to scale back TCAM usage. Recently, many works have been presented on this subject because of 2 opportunities. One is the well-known vary growth problem for packet classifiers to be stored in TCAM entries. The other is that there often exists redundancy among rules. During this paper, we have a tendency to propose a unique technique called Block Permutation (BP) to compress the packet classification rules stored in TCAMs. In contrast to previous schemes that compress classifiers by converting the initial classifiers to semantically equivalent classifiers, the BP technique innovatively finds semantically nonequivalent classifiers to realize compression by performing block-primarily based permutations on the foundations represented in Boolean House. We tend to have developed an efficient heuristic approach to seek out permutations for compression and have designed its hardware implementation by employing a field-programmable gate array (FPGA) to preprocess incoming packets. Our experiments with ClassBench classifiers and Internet Service Provider (ISP) real-life classifiers show that the proposed BP technique will considerably reduce thirty one.88% TCAM entries on average, in addition to the reduction contributed by different state-of-the-art schemes.


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