Design of a Message Passing Model for Use in a Heterogeneous CPU-NFP Framework for Network Analytics

Abstract

Currently, network analytics requires direct access to network packets, normally through a third-party application, which means that obtaining realtime results is difficult. We propose the NFP-CPU heterogeneous framework to allow parts of applications written in the Go programming language to be executed on a Network Flow Processor (NFP) for enhanced performance. This paper explores the need and feasibility of implementing a message passing model for data transmission between the NFP and CPU, which is the crux of such a heterogeneous framework. Architectural differences between the two domains are highlighted within this context and we present a solution to bridging these differences.