## 2 In-Network Computing: What and Why
### Enabling Technologies
The synergy among 3C resources underpins the transition from static infrastructure to an intelligent, self-orchestrating system capable of meeting the demands of next-generation applications. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Technology** & **Main Features** & **Contributions to INC** \\ \hline \multirow{5}{*}{SDN} & \(\bullet\) & Decouples control/data planes and & \(\bullet\) & Enables dynamic resource allocation and \\  & & implements a centralized network & \(\quad\) & task scheduling. \\  & & controller. & \(\quad\) & \(\bullet\) \\  & \(\bullet\) & Provides high-level programmability & \(\quad\) & \(\bullet\) \\  & & with APIs. & \(\quad\) & \(\bullet\) \\  & \(\bullet\) & Abstracts physical infrastructure into a & \(\quad\) & capable nodes. \\  & & unified interface. & \(\quad\) & \(\bullet\) \\ \hline \multirow{5}{*}{NFV} & \(\bullet\) & Virtualizes network functions on & \multirow{5}{*}{\begin{tabular}{l} Facilitates instantiation and migration of \\ functions on diverse devices. \\ Maximizes resource utilization across \\ shared infrastructure. \\ \end{tabular} } \\  & \(\bullet\) & Abstracts hardware for multi-tenancy. & \\ \hline \multirow{5}{*}{ICN} & \(\bullet\) & Focuses on content-based rather than & \multirow{5}{*}{\begin{tabular}{l} Reduces latency by processing and \\ caching data in-network. \\ Enables dynamic task distribution closer \\ to clients. \\ \end{tabular} } \\  & \(\bullet\) & Provides efficient content retrieval & \(\quad\) & \(\bullet\) \\  & \(\bullet\) & mechanisms. & \\ \hline \multirow{5}{*}{Programmable switches, smart NICs, FPGAs} & \(\bullet\) & \
\begin{tabular}{l} Offer high-speed processing within the \\ network. \\ Enable customizable hardware \\ acceleration. \\ \end{tabular} & \(\bullet\) &
\begin{tabular}{l} Transform network elements into active \\ computation participants. \\ Facilitate real-time in-network execution \\ of tasks. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Enabling technologies for INC. It is worth observing that INC will not replace cloud and edge computing; rather, these paradigms will coexist and complement each other, forming a cohesive ecosystem that leverages the strengths of centralized cloud resources, edge proximity, and in-network processing to meet the diverse and stringent requirements of 6G applications. Figure 1 summarizes the overall vision, where every network entity along the cloud-to-end-devices continuum is augmented with 3C capabilities to enable INC. Numerous research initiatives and projects have emerged to promote and advance INC. Among them, the Computing in the Network Research Group ([[https://www.irtf.org/coinfo.html](https://www.irtf.org/coinfo.html)]([https://www.irtf.org/coinfo.html](https://www.irtf.org/coinfo.html)), accessed on 8 January 2024) (COINRG), established in 2019 under the Internet Research Task Force (IRTF), focuses on developing architectures, protocols, and implementations that tightly integrate computing and networking. Similarly, the IETF Routing Area Working Group ([[https://datatracker.ietf.org/group/rtgwg/about](https://datatracker.ietf.org/group/rtgwg/about)]([https://datatracker.ietf.org/group/rtgwg/about](https://datatracker.ietf.org/group/rtgwg/about)), accessed on 8 January 2024) (RTGWG) has also contributed to the INC research by presenting the compute first networking (CFN) framework [36], where service requests are routed to optimal edge nodes based on real-time assessments of computing resources and network conditions. In the same context, the Programming Protocol-independent Packet Processors (P4) Project ([[https://p4.org/](https://p4.org/)]([https://p4.org/](https://p4.org/)), accessed on 8 January 2024) aims to standardize a high-level programming language for precise control of packet processing, enabling highly customizable in-network operations. According to [19], INC applications can be broadly classified into five main categories:  
1. In-Network Analytics: Data-intensive tasks such as data aggregation and machine learning. 2.