Kenichi Yasukata

Abstract

This paper presents iip, an integratable TCP/IP stack, which aims to become a handy option for developers and researchers who wish to have a high-performance TCP/IP stack implementation for their projects. The problem that motivated us to newly develop iip is that existing performance-optimized TCP/IP stacks often incur tremendous integration complexity and existing portability-aware TCP/IP stacks have significant performance limitations. In this paper, we overhaul the responsibility boundary between a TCP/IP stack implementation and the code provided by developers, and introduce an API that enables iip to allow for easy integration and good performance simultaneously, then report performance numbers of iip along with insights on performance-critical factors.

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Changgang Zheng, Mingyuan Zang, Xinpeng Hong, Liam Perreault, Riyad Bensoussane, Shay Vargaftik, Yaniv Ben-Itzhak, Noa Zilberman

Abstract

In-network machine learning inference provides high throughput and low latency. It is ideally located within the network, power efficient, and improves applications’ performance. Despite its advantages, the bar to in-network machine learning research is high, requiring significant expertise in programmable data planes, in addition to knowledge of machine learning and the application area. Existing solutions are mostly one-time efforts, hard to reproduce, change, or port across platforms. In this paper, we present Planter: a modular and efficient open-source framework for rapid prototyping of in-network machine learning models across a range of platforms and pipeline architectures. By identifying general mapping methodologies for machine learning algorithms, Planter introduces new machine learning mappings and improves existing ones. It provides users with several example use cases and supports different datasets, and was already extended by users to new fields and applications. Our evaluation shows that Planter improves machine learning performance compared with previous model-tailored works, while significantly reducing resource consumption and co-existing with network functionality. Planter-supported algorithms run at line rate on unmodified commodity hardware, providing billions of inference decisions per second.

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