Parallel many fields packet classification technique using R-tree

Abstract

Network packet classification is important network kernel function to enables various network services such as Quality of Service (QoS), security and resource reservation. The OpenFlow protocol is the responsible of the packet classification, it uses a set of rules called ruleset, each rule in that rule set contain a set of matching fields. With the rapid growing of rulesets size and rule fields numbers in the modern networks, it became so difficult to classify incoming packets at reasonable speed using the classical packet classification techniques. Many modern software-based classification solutions have been proposed to accelerate packet classification in additional to hardware-based solutions. In general, to design packet classification algorithm, it is important to strike a balance between high throughput and low memory requirements. This paper proposes an R-tree based parallel many-field packet classification technique using multi-threading to classify packet into the flow it belongs to; Multi-threading has been employed to accelerate packet classification by introduce a parallelism packet classification scheme. Two main algorithms that represent the main two parts of the proposed technique have been described, first algorithm describes how to constructing R-tree from the corresponding ruleset, and the other algorithm describes the R-tree querying in parallel manner. The performance of the proposed technique has been evaluated with consideration of throughput, latency, and memory access by testing the proposed many-fields packet classification algorithms on Class-Bench rulesets. Experiment results showed that the proposed parallel query algorithm can classify packets with very good throughput, latency, and memory access compared with other many-fields packet classification solutions.