# A3C-R: A QoS-Oriented Energy-Saving Routing Algorithm for Software-Defined Networks
## 2 Related Research  
With the continuous diversification of business traffic requirements and the increasing network energy consumption, routing traffic scheduling for QoS and energy saving is facing severe challenges. Due to the continuous development of SDN and artificial intelligence technology, many domestic and foreign researchers continue to propose intelligent traffic scheduling methods based on machine learning in the SDN environment. 1. Research Status of QoS Routing Technology  
QoS routing aims to provide differentiated services under different business traffic requirements, network operating status and link available resources, and dynamically select transmission paths that meet network load and resource configuration for different business flows. With the rise in SDN, SDN-oriented QoS routing calculations continue to develop. [PERSON] [8] proposed a routing optimization technology solution based on traffic classification in the SDN environment. This solution can identify and classify the service types of data flows entering the network and perform routing optimization scheduling according to the QoS requirements corresponding to the service types of data flows, so as to provide a QoS guarantee for network applications of different service types. Compared with the traditional scheduling mechanism, it can effectively reduce network congestion and improve network service quality. However, the calculation efficiency of QoS of this algorithm still needs to be improved under complex network traffic. In order to further improve the computational efficiency of the QoS routing algorithm, some researchers have introduced the classic heuristic algorithm into SDN for research. [PERSON] et al. [9] proposed a novel energy efficient QoS secure routing algorithm based on the Levy Chaos Adaptive Snake Optimization-based Multi-Trust Routing Method (LCASO-MTRM). The method combines chaotic and adaptive operators and aims to reduce energy consumption, latency, and packet loss while increasing bandwidth. By introducing the link trust mechanism, LCASO-MTRM is able to more accurately assess the link trust level and reflect the current link status. Although the above QoS routing algorithms have better QoS performance than the classical Dijkstra routing algorithm, it is difficult to consider the balance of QoS parameters more comprehensively in the case of more complex business traffic. With the rise in artificial intelligence, [PERSON] et al. [10] proposed a Dynamic Fuzzy Routing Algorithm based on Deep Reinforcement Learning (DFRDRL). This involved applying deep reinforcement learning for path routing decisions with the help of Target and Online Neural Networks (TONN). It also introduces a delay mechanism to detect congestion based on real-time traffic forecasts and prioritize low resource demand requests. The method achieves a significant improvement in optimizing network QoS performance such as delay, bandwidth, and link utilization. [PERSON] et al. [11] proposed a QoS-driven routing optimization method (DQS) based on Deep Reinforcement Learning (DRL) for routing optimization in Software Defined Network (SDN). The approach dynamically adapts routing decisions through a multi-objective function-driven DRL agent that combines link and queuing metrics to reduce end-to-end delay and improve network performance. However, when facing multiple traffic types, it is difficult to meet the QoS requirements of different applications. 2. Research status of energy-saving routing technology  
Energy-saving routing is a key means to solve the problem of network energy consumption. It is of great significance to reduce network operation costs and improve the efficiency of network resource utilization. The exploration of its optimization strategy has always been a research hotspot in the field of network research. [PERSON] et al. [12] proposed an unequally clustered routing protocol based on a multi-hop threshold distance (UCRTD).