Packet Classifier LaKe FPGA-NIC Memcached Traffic Normal Traffic Kernel Space User Space memcached Cache Hit Client Network Interface DMA DRAM BRAM Client Client Client Cache Miss

Programmable network hardware can run services traditionally deployed on servers, resulting in orders-of-magnitude improvements in performance. Yet, despite these performance improvements, network operators remain skeptical of in-network computing. The conventional wisdom is that the operational costs from increased power consumption outweigh any performance benefits. Unless in-network computing can justify its costs, it will be disregarded as yet another academic exercise. In this paper, we challenge that assumption, by providing a detailed power analysis of several in-network computing use cases. Our experiments show that in-network computing can be extremely power-efficient. In fact, for a single watt, a software system on commodity CPU can be improved by a factor of ×100 using an FPGA, and a factor of ×1000 utilizing ASIC implementations. However, this efficiency depends on the system load. To address changing workloads, we propose in-network computing on demand, where services can be dynamically moved between servers and the network. By shifting the placement of services on-demand, data centers can optimize for both performance and power efficiency. CCS Concepts • Networks In-network processing; • Hardware Power estimation and optimization. ACM Reference Format: Yuta Tokusashi, Huynh Tu Dang, Fernando Pedone, Robert Soulé, and Noa Zilberman. 2019. The Case For In-Network Computing On Demand. In Proceedings of Fourteenth EuroSys Conference 2019 (EuroSys ’19). ACM, New York, NY, USA, 16 pages. https: //doi.org/10.1145/3302424.3303979 Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. EuroSys ’19, March 25–28, 2019, Dresden, Germany © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-6281-8/19/03. . . $15.00 https://doi.org/10.1145/3302424.3303979