Network flow classification is a key function in high-speed switches and routers. It directly determines the performance of network devices. With the development of the Internet and various kinds of applications, the flow classification needs to support multi-dimensional fields, large rule sets, and sustain a high throughput. Software-based classification cannot meet the performance requirement as high as 100 Gbps. FPGA-based flow classification methods can achieve a very high throughput. However, the range matching is still challenging. For this, this paper proposes a range supported bit vector (RSBV) method. First, the characteristic of range matching is analyzed, then the rules are pre-encoded and stored in memory. Second, the fields of an input packet header are used as addresses to read the memory, and the result of range matching is derived through pipelined Boolean operations. On this basis, bit vector for any types of fields (AFBV) is further proposed, which supports the flow classification for multi-dimensional fields efficiently, including exact matching, longest prefix matching, range matching, and arbitrary wildcard matching. The proposed methods are implemented in FPGA platform. Through a two-dimensional pipeline architecture, the AFBV can operate at a high clock frequency and can achieve a processing speed of more than 100 Gbps. Simulation results show that for a rule set of 512-bit width and 1[Formula: see text]k rules, the AFBV can achieve a throughput of 520 million packets per second (MPPS). The performance is improved by 44% compared with FSBV and 30% compared with Stride BV. The power consumption is reduced by about 43% compared with TCAM solution.
A Preliminary Performance Evaluation of K-means, KNN and EM Unsupervised Machine Learning Methods for Network Flow Classification
