## 2 (Com)2 INet: Communication and Computation Integrated Network Architecture
### System Architecture
Converged Network Layer takes network component as the basic unit and is mainly responsible for data storage, forwarding, and computing, including network devices, computing devices, and integrated devices. The node identifier (NID) is used to identify the network component, and the node behavior description (NBD) is used to identify network functions. To be specific, network devices such as switches, routers, and gateways constitute the core network and are mainly responsible for data storage and forwarding. The computing devices are composed of one or more processing units such as central  
Figure 2: Functional architecture of the designed (Com)2 INet. processing unit (CPU), network processing unit (NPU), field programmable gate array (FPGA), and graphics processing unit (GPU), which are responsible for processing real-time tasks [24]. Computing devices are distributed at the edge of the network, including multiple computing centers, compared with network devices. Integrated devices integrate computing, storage, and forwarding functions. For instance, router devices can have storage, forwarding, and computing functions by introducing the computing function. When the computing-enhanced router device receives a service, it can process the service locally or forward it to other appropriate devices. Note that network components support heterogeneous network protocol communications including IPv4, IPv6, etc., enabling a cross-protocol collaborative transmission mechanism. Mapping Adaption Layer takes the function as the basic unit and is mainly responsible for scheduling multi-dimensional resources. The function identifier (FID) is used to identify the function types, and the function behavior description (NBD) is used to identify function functions. To be specific, there are three computing paradigms that support the implementation of computing services, in terms of in-network computing [25], out-of-network computing [26], and end-edge-cloud collaborative computing [27]. In-network computing can directly execute lightweight computing tasks in network devices, such as switches and routers, along the task transmission path, instead of transmitting data to traditional computing nodes, such as servers and cloud platforms. Out-of-network computing, with powerful computing capabilities, can orchestrate heavy-weight computing tasks to data centers and supercomputer centers for execution. Moreover, a computing task can be decomposed into multiple micro-services, or a monolithic function can be divided into multiple portions to orchestrate different parts between ends, edges, and clouds. For instance, the deep neural network (DNN) model can be partitioned, where the shallow layers are deployed and performed on the ends, the middle layers are deployed and performed on edges, and the other layers are deployed and performed on the cloud. Between the computing service layer and the mapping adaption layer, a behavior matching mechanism is used. The dynamic matching process from computing services to functions is formulated as below. \[ \begin{bmatrix}FID_{1}\\ FID_{2}\\ \vdots\\ FID_{s}\end{bmatrix} \triangleq\Omega\left( \begin{bmatrix}CID_{i},CBD_{i}\\ CID_{i},CBD_{i}\end{bmatrix} , \begin{bmatrix}FID_{1},FBD_{1}\\ FID_{2},FBD_{2}\\ \vdots\\ FID_{m},FBD_{m}\end{bmatrix} \right), \tag{1}\]  
where \([FID_{1},FID_{2},\cdots,FID_{s}]\) is the group identifier of the \(s\) functions selected according to the requirements of on-demand networking. \([CID_{i},CBD_{i}]\) represents the \(i\)-th computing service, \([(FID_{1},FBD_{1}),(FID_{2},FBD_{2}),\cdots,(FID_{m},FBD_{m})]\) are \(m\) pairs of FID and FBD in the network, and \(\Omega(\cdot)\) is the behavior matching mapping function. Between the mapping adaptation layer and the converged network layer, a behavior clustering mechanism is used. The intelligent clustering process from functions to network components is formulated as below. \[