Abstract
This paper addresses the problem of packet classification within a network processor (NP) architecture without the separate associative device. By the classification, we mean the process of identifying a packet by its header. The classification stage requires the implementation of data structures to store the flow tables. In our work, we consider the NP without the associative memory. Flow tables are represented by an assembly language program in the NP. For translating flow tables into assembly language programs, a tables translator was used. The main reason for implementing data compression algorithms in a flow tables translator is that modern flow tables can take up to tens of megabytes. In this paper, we describe the following data compression algorithms: Optimal rule caching, recursive end-point cutting and common data compression algorithms. An evaluation of the implemented data compression algorithms was performed on a simulation model of the NP.
Highlights
At present, software-defined networks (SDN) are in active development and require highperformance switches [1]
We need to develop an algorithm for compressing flow tables. This algorithm must translate an input flow table into a new compressed set of rules . 1) The set of rules is similar to the set of rules . 2) The cardinality of the set must be lower than the cardinality of the set
It is used for flow tables translating into assembly language programs, that can be interpreted by network processor (NP)
Summary
Software-defined networks (SDN) are in active development and require highperformance switches [1]. The main functional element of the high performance SDN switch is a programmable network processor (NP). We discuss data compression algorithms used for flow tables. Flow tables are needed for packet classification process. A flow table is the set of rules defined by OpenFlow protocol. OpenFlow is one of the most common protocols for controlling a network SDN switch. Classification is the process of the identification of a network packet by its header. This article has the following structure: in second section we introduce problem, in third section we introduce the NP architecture and flow tables translator, in fourth section we describe related work, in fifth section we describe data compression algorithm implementation and in sixth section we introduce our evaluation methodology
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