RVH: Range-Vector Hash for Fast Online Packet Classification

TL;DR

RVH is a novel hash-based packet classification method that significantly improves classification and update speeds while maintaining reasonable memory usage, addressing the need for flexible, high-speed network policy management.

Contribution

The paper introduces Range-vector Hash (RVH), a new approach that balances fast packet classification and rule updating by leveraging range-vectors to optimize hash table usage.

Findings

Achieves up to 15.7x faster classification speed

Achieves up to 2.3x faster rule update speed

Uses 44% less memory compared to state-of-the-art algorithms

Abstract

Packet classification according to multi-field ruleset is a key component for many network applications. Emerging software defined networking and cloud computing need to update the rulesets frequently for flexible policy configuration. Their success depends on the availability of the new generation of classifiers that can support both fast ruleset updating and high-speed packet classification. However, existing packet classification approaches focus either on high-speed packet classification or fast rule update, but no known scheme meets both requirements. In this paper, we propose Range-vector Hash (RVH) to effectively accelerate the packet classification with a hash-based algorithm while ensuring the fast rule update. RVH is built on our key observation that the number of distinct combinations of each field prefix lengths is not evenly distributed. To reduce the number of hash tables for fast classification, we introduce a novel concept range-vector with each specified the length range of each field prefix of the projected rules. RVH can overcome the major obstacle that hinders hash-based packet classification by balancing the number of hash tables and the probability of hash collision. Experimental results demonstrate that RVH can achieve the classification speed up to 15.7 times and the update speed up to 2.3 times that of the state-of-the-art algorithms on average, while only consuming 44% less memory.