# DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control
## 3 Performance Evaluation  
While testing and comparing various congestion controls, we vary the bandwidth of the route by changing the bandwidth of the router interface. Similarly, we insert a certain percentage of loss into our emulator nodes by using the Linux traffic control for the interface on that route. We run several _iPerf3_ tests in network scenarios shown in Figure 2 by varying the bandwidth and varying the percentages of losses without changing other network parameters (along the lines of [5]). Unless otherwise specified, all path latencies, bandwidths, and buffer capacities are 40 ms, 100 Mbps, and 400 packets, respectively. Along the lines of MPCC [5], in Figures 4 and 5, we compare MPCC with the Linux kernel-implemented MPTCP versions Lia, Olia, and Balia, as well as with the use of singlepath TCP (Reno and BBR) for each subflow. The details about Linux kernel-implemented MPTCP versions (Lia, Olia, and Balia) and TCP (Reno, Cubic, and BBR) are explained in [5; 7] and therefore we include essential details only. We repeat the experiment outlined in Network Scenario a, the multipath sender competes with a single path sender (PCC vs. MPCC, TCP Reno vs. MPTCP). As shown  
Figure 4: Throughput comparison of one of the shared paths of MPCC with the state-of-the-art MPTCP and TCP protocols over the variation in the size of bottleneck buffer in the path (network scenario a). Figure 5: Throughput comparison of MPCC with the state-of-the-art MPTCP and TCP protocols over the variation in the percentage of path loss (network scenario b). in Figure 4, when a multipath protocol fails to exploit path 1 effectively, it will become more aggressive on path 2, resulting in a decrease in good performance for the single path connection. As shown in Figure 5, MPTCP responds to packet loss by scaling down the sending rate multiplicatively. In sharp contrast, MPTC maintains a higher throughput across the full range and only drops performance when the loss rate is several times greater than that, which has the equivalent effect on MPTCP. These observations have been partly reported in [5], all of which have been extensively experimented with, and the logs are gathered to generate the data sets required to train the DDPG module. By implementing the three different scenarios shown in Figure 2, the internet traffic and performance parameters are captured through custom logging functions placed within the kernel to ensure accurate data analysis. This is to prepare sufficient data for offline training and setting up the input environment of the DDPG module. For simplicity, considering the data distribution of the inputs for DDPG training, we normalized values (between 0 and 1) to simplify the learning process. Based on our observations across several experimental runs, the performance logs have drastic variations, and normalization allows us to eliminate such skewed data. It is known in the DDPG literature that such an approach helps to improve the convergence of training dynamics and achieves better stability. After gathering training data from the kernel logs, we train our DDPG-MPCC using the three networks and several runs. In the offline training procedure, we ensure that each training episode begins with a new data point and always converges. We then tested our trained model for 100 MIs using the test dataset obtained in the same fashion when DDPG-MPCC competes with MPTCC across the network in scenario c) under the same parameter settings. In each MI, we calculate the tuples (average sending rates, average utility, and average reward) and plot them to examine how the sending rate and utility of DDPG-MPCC and MPTC improve with MIs. Figures 6-8 illustrate the results. We can see in Figure 6 that the average sending rate of DDPG-MPCC is very low at the start, with a bit of contention with MPTC for some initial MIs. It demonstrates substantial increments over higher MIs and attains the maximum without any visible (adverse) impacts on the dynamics and performance of the competing MPTC rates.