## 4 RSCR
### Architecture  
(1) _The Data Plane:_ The data plane is a collection of all infrastructure, including end devices, forwarding devices, and the links connecting them. Responsible for the actual processing and forwarding of data packets, it utilizes the programmability and real-time response of flow table rules. It executes instructions from the control plane, implements RSCR routing policies, and responds to the periodic transmission of computing resources  
Figure 1: RSCR Architecture. and network resource information by the computing resource perception unit and network resource awareness unit. (2) _The Control Plane_: The control plane is a core component of the architecture, responsible for formulating network policies and dynamic management, constructing a global view of the data plane. Separated from the data plane, it uses a centralized controller to manage and adjust flow table rules of network devices in real-time, responding to the requirements of different applications and services to optimize network performance, security, and availability. This plane mainly consists of two modules: the network resource awareness unit and the computing route deployment unit. (3) _The Management Plane_: The management plane is a crucial component of the SDN architecture, responsible for centralized network management and control. It provides network administrators with an abstract way to define network policies, configure devices, monitor network performance, and apply intelligent decision-making, achieving flexibility, automation, and intelligent management of the network. The management plane collaborates with the data plane, where the computing unit processes collected raw data on computing resources and network resources to segment computing nodes and calculate available bandwidth, latency, and packet loss rates. These data describe the computational capabilities and network status of computing nodes. (4) _The Knowledge Plane_: The knowledge plane is a core component of RSCR, designed to integrate and manage widely distributed knowledge sources to support intelligent decision-making and reasoning. It provides functions for the storage, retrieval, inference, and application of knowledge, enabling the system to automatically analyze and understand information for better meeting user needs and supporting intelligent decision-making. This plane, through the management plane, processes well-handled computing-network resource data and computing task requests proposed by the data plane to calculate and install the optimal route. The deployment of the knowledge plane is highly flexible; it can be deployed on top of the control plane, similar to the application plane in standardized SDN [41], or deployed separately [34]. RSCR opts for a separate deployment of the knowledge plane to avoid overloading the control plane. (5) _The Computing Resource Perception Unit:_ This unit is responsible for perceiving the computing resources of each computing node in the computing resource end and sending the perceived raw data to the computing-network resource processing unit of the management plane. The computing resource perception process is illustrated in Figure 2, involving three main components: Client, Master, and Cluster. The Master is in charge of the entire Kubernetes cluster, composed mainly of API-Server, kube-scheduler, kube-controller-manager, and cloud-controller-manager. Additionally, the Metrics-Server is deployed on the Master. Nodes receive instructions from the Master to carry out tasks. It is noteworthy that the Metrics-Server is not part of the API-server; it is independently deployed based on the Aggregator plugin mechanism, providing services uniformly to the API-server. By accessing the /api/metrics.k8s.io/v1 beta1 interface exposed by Metrics-Server, periodic data can be obtained. These data are collected from the kubelet's Summary API and include both cAdvisor's monitoring data and kubelet's summary information.