A Reinforcement Learning-Based Routing for Real-Time Multimedia Traffic Transmission over Software-Defined Networking

The resulted data were obtained by implementing the proposed architecture on Ubuntu 16.04 installed in an HP Z230 tower workstation with an Intel Xeon processor and 16 GB RAM. Mininet emulator is used to run the infrastructure plane, which includes the SDN-enabled video streaming network. RYU Controller utilised to emulate the control plane, which collects information about network topology to obtain the environment states. Three realistic network topologies are used for the experimental evaluation of our approach; a modified Abilene topology, Geant, and Cernet. The topologies have been built and implemented in Mininet using a Python script; SDN-Openflow switches replaced the nodes for each network topology. Each switch has a host that forwards and receives different types of traffic. Multimedia providers are deployed in a number of Openflow switches. The provider is able to stream real-time Dynamic Adaptive Streaming over HTTP (DASH) based video flows. DASH video is divided into 4s chunks encoded into five discrete bit rates ranging from 260 Kbps to 2998 Kbps using FFmpeg version 4.3.2 with the H.264 codec, and segmented based on GPAC MP4Box in order to create the DASH manifest and associated files. The video content streamed by multimedia providers is the “Big Buck Bunny” animation with a 1920 × 1080 pixels resolution and was cut into 5 min long. The selection of hosts that partake in the experiment has been constructed to enable the traffic flow to pass through the whole network topology. In the meantime, the study has utilised Wireshark as video traffic monitoring software in the end user's device in order to capture the received video segments during video streaming. The OSPF-based approach is compared with our solution to evaluate customers' desired satisfaction across real-time DASH video streaming. The provided files present the performance comparison of the proposed RL-based solution with the OSPF protocol in terms of the customers’ satisfaction represented by Multimethod Assessment Fusion (VMAF) and Structural Similarity Index Metric (SSIM), and network throughput.

本数据集的实验数据，均通过在搭载英特尔至强（Intel Xeon）处理器、16GB内存的HP Z230塔式工作站，部署Ubuntu 16.04操作系统并运行所提架构后获取。实验采用Mininet仿真器搭建基础设施平面，该平面包含支持软件定义网络（SDN）的视频流网络；同时依托RYU控制器模拟控制平面，其可采集网络拓扑信息以获取环境状态。本次实验评估共采用三类真实网络拓扑：改进型Abilene拓扑、Geant拓扑与Cernet拓扑。所有拓扑均通过Python脚本在Mininet中构建完成，且将各拓扑中的节点替换为SDN-OpenFlow交换机。每台交换机均挂载一台主机，用于转发与接收各类流量。多台OpenFlow交换机上部署了多媒体提供商节点，该节点可基于超文本传输协议动态自适应流媒体（Dynamic Adaptive Streaming over HTTP, DASH）传输实时视频流。所使用的DASH视频经FFmpeg 4.3.2版本配合H.264编解码器，被切割为4秒时长的片段，并编码为260 Kbps至2998 Kbps共5档不同码率；随后通过GPAC MP4Box进行切片处理，以生成DASH播放清单及关联文件。多媒体提供商所传输的视频内容为分辨率1920×1080像素的《大雄兔》（Big Buck Bunny）动画片段，经剪辑为5分钟时长。实验所选参与流量传输的主机，可确保数据流遍历整个网络拓扑。与此同时，本研究在终端用户设备中使用Wireshark作为视频流量监控软件，以捕获视频流传输过程中接收的视频片段。为评估实时DASH视频流场景下用户的期望满意度，本研究将基于开放式最短路径优先（OSPF）的传统路由方案与所提方案进行对比。本数据集包含所提强化学习（Reinforcement Learning, RL）方案与OSPF协议的性能对比结果，评估指标包括以多方法评估融合（Multimethod Assessment Fusion, VMAF）与结构相似性指数（Structural Similarity Index Metric, SSIM）表征的用户满意度，以及网络吞吐量。