# A3C-R: A QoS-Oriented Energy-Saving Routing Algorithm for Software-Defined Networks
## 5 Experimental Evaluation
### Performance Analysis of Routing Algorithms  
This experiment sets up the comparison of routing QoS and energy-saving indicators under different network loads. Since the link bandwidth of the experimental topology is 200 Mbps by default, we increase the traffic load from 20 Mbps to 180 Mbps. We used a combination of different types of traffic workloads to mimic real-world network traffic. Specifically, we employed three main types of traffic: delay-sensitive traffic, which is characteristic of real-time applications like online gaming, video conferencing, and instant messaging; bandwidth-sensitive traffic, representing services such as high-definition video streaming and large-file transfers; and packet loss-sensitive traffic, corresponding to applications such as financial transaction data transmission and medical data transfer. These traffic types were randomly generated and mixed in certain proportions to create a complex and realistic traffic load. During the experiment, we tested QoS indicators such as delay, throughput, and packet loss rate under different network traffic loads, as well as network energy-saving effects. We use the percentage of energy saving (PES) of the network link to evaluate the energy efficiency of the routing algorithm. PES is defined as the percentage reduction in energy consumption achieved by the proposed algorithm compared to a baseline energy consumption, typically the total network energy consumption under full link load. The specific calculation process is shown in the Formula (22) [23]. Where \(\phi_{f-routing\ algorithm}\) represents the link energy consumption of the current routing algorithm, while \(\phi_{f-full\ load}\) represents the energy consumption under full link load conditions. A higher PES indicates better energy-saving performance. After many experiments, we found that parameter \(\alpha\) affects the dynamic energy consumption part of the link. When the \(\alpha\) value is too small, the algorithm is not sensitive enough to the link traffic changes to optimize the energy consumption effectively; when it is too large, it will pay excessive attention to the energy consumption at the expense of the QoS performance. It has been tested that setting \(\alpha\) to 2 optimizes network power consumption to the maximum extent while ensuring QoS. The parameter \(\mu\) affects the link intrinsic energy consumption part, its value is too small, and the algorithm does not take into account the link intrinsic energy consumption enough; when it is too large, it will lead to the unreasonable allocation of network resources and affect the QoS. Setting \(\mu\) to 1 enables a better balance between considering the inherent energy consumption of the link and the dynamic energy consumption, ensuring that the algorithm optimizes energy consumption while maintaining good network performance. Therefore, In the energy consumption function, parameter \(\alpha\) is set to 2, and parameter \(\mu\) is set to 1. The routing algorithm performance test results are shown in Figure 5.  
\[PES=1-\frac{\phi_{f-routing\ algorithm}}{\phi_{f-full\ load}}*\% \tag{22}\]  
Figure 4: Reward values of training process. It can be seen from Figure 5 that as the traffic load continues to increase, the A3C-R routing algorithm is superior to other routing algorithms in terms of QoS performance indicators such as delay, throughput, and packet loss rate, as well as energy saving effects. The reason is that the traditional routing algorithm ECMP mainly uses equal-cost multi-path transmission, and does not comprehensively consider the delay, throughput, packet loss rate and energy performance indicators of each path in the current network. It is easy to cause link congestion when the network traffic increases. The I-DQN algorithm considers multiple types of QoS requirements in the routing algorithm modeling process, so the algorithm has better optimization effects such as delay, throughput, and packet loss rate compared with the ECMP algorithm.