Programmable Data Planes meets In-Network Computing: A Review of the State of the Art and Prospective Directions


  • Leonardo Reinehr Gobatto Federal University of Rio Grande do Sul (UFRGS) - Brazil
  • Pablo Rodrigues Federal University of Rio Grande do Sul (UFRGS) - Brazil
  • Mateus Saquetti Pereira de Carvalho Tirone Federal University of Rio Grande do Sul (UFRGS) - Brazil
  • Weverton Luis da Costa Cordeiro Federal University of Rio Grande do Sul (UFRGS) - Brazil
  • José Rodrigo Furlanetto Azambuja Federal University of Rio Grande do Sul (UFRGS) - Brazil



In-Network Computing, Domain-Specific Languages, Hardware Accelerator


Improving network traffic in networks is one of the concerns between networking researchers and network operators since the architecture of modern networks still faces challenges to process large data traffic without the cost of consuming a significant amount of resources not related to computing specifically. On the other hand, network programmability has enabled the development of new applications and network services, from software-defined networking to domain-specific languages created to program network devices and specify their behavior. The development of programmable hardware and hardware accelerators like FPGAs, GPUs, and CPUs help this new paradigm go one step further. Use the artifact of programmability of these devices to solve problems, such as improve the processing of data traffic is the key of in-network computing. It offers the opportunity to execute programs typically running on end-hosts within programmable network devices already incorporated on the network, thus being capable of provides a reduction on the in-network processing load and requires no extra cost, since operations can be concluded using a fewer amount of devices of the network and no extra device are needed. In this paper, we survey in-network computing, as well as we suggest classifying related works to in-network computing according to the hardware accelerator used. Also, we discuss challenges and research directions.






Special Issue on Intelligent, Energy-Efficient and Robust Computing Systems