Int. J. Cloud Computing, Vol. 9, No. 1, 2020 55 In-network processing for edge computing with InLocus Lucas R.B. Brasilino Intelligent Systems Engineering – ISE, School of Informatics, Computing and Engineering – SICE, Indiana University, Bloomington, IN, USA Email: lbrasili@iu.edu Naveen Marri Computer Science – CS, School of Informatics, Computing and Engineering – SICE, Indiana University, Bloomington, IN, USA Email: navmarri@iu.edu Alexander Shroyer, Catherine Pilachowski, Ezra Kissel and Martin Swany* Intelligent Systems Engineering – ISE, School of Informatics, Computing and Engineering – SICE, Indiana University, Bloomington, IN, USA Email: ashroyer@iu.edu Email: calupila@iu.edu Email: ezkissel@iu.edu Email: swany@iu.edu *Corresponding author Abstract: As sensors and smart device infrastructure grows, networks are increasingly heterogeneous and diverse. We propose an efficient and low-latency architecture called InLocus, which facilitates stream processing at the network’s edge. InLocus balances hardware-accelerated performance with the flexibility of asynchronous software control. In this paper, we extend InLocus architecture by implementing compute nodes in a more traditional cloud-based solution in the form of Apache Kafka and Twitter Heron framework, as well as by introducing a new runtime approach for the previously handwritten C Server. We utilise a flexible platform (Xilinx Zynq SoC) to compare microbenchmarks between the latter and high-level synthesis (HLS) version in programmable hardware. Keywords: in-network processing; edge computing; internet of things; IoT; programmable logic; FPGA; offloading; hardware acceleration. Copyright © 2020 Inderscience Enterprises Ltd.