Distributed predictive QoS in presence of network- and mobility-related drifts

The datasets represent a dynamic environment, where several client-vehicles are moving in an urban area. Each client runs a streaming cloud service constantly receiving data packets. Network simulation is performed using Simu5G, a library that emulates a 5G cellular environment in OMNeT++. The simulator's radio parameters are set according to the Macro-cell model proposed by International Telecommunication Union. The map comprises of an urban 600x600 square meters area located in a suburb of a European capital. Inside this area four 5G base-stations (gNodeBs) have been installed by the national network operator, enabling four 5G cells. This area, divided into several blocks by the actual road network is integrated in our simulation by an OpenStreetMap (OSM) instance. The total number of included vehicles is set to 25, according to vehicle density statistics in the corresponding country. The road network's traffic is simulated by SUMO that creates a digitized version of the (real-world) OSM map and produces the route files for the vehicles. Route files are loaded in the Simu5G simulator, where a network-vehicular mobility co-simulation takes place. For each vehicle's route we assume SUMO's default parameters for urban environment: exponential speed model (with maximum speed restriction as defined by the OSM traffic rules) and the probability matrix at intersections for {lane keeping, turn left and right} as {0.5, 0.25 and 0.25}, respectively. The following information is collected for each vehicle using OMNeT++'s monitoring service: timestamp, channel quality indicator, packet delay, measured signal to noise ratio (SNR), client position (x,y,z), client velocity (x,y,z), received SNR, radio link control throughput, serving cell, client throughput. These features are sampled at 1 Hz and comprise the values of our synthetic time-series QoS dataset. We have created two drift datasets that correspond to complementary cases of major long-term changes in the considered environment: 1) a network infrastructure-driven scenario (Sc1) and 2) a human behavior-driven scenario (Sc2). In Sc1 we assume that two out of four gNodeBs are switched off under a cost-reduction on/off policy or an infrastructure-share strategy (adopted by MNOs) that would imply such changes. For Sc2 we modify the users' mobility pattern; we assume that a "hotspot" e.g., a metro station is created in the lower-right edge of the map resulting in a traffic increase to that area. This is achieved by increasing the probabilities of the routes leading to the "hotspot" in SUMO's route planning. All generated datasets have a total duration of 20 hrs (simulation time) and the respective drift event is introduced at t=10 hrs.本数据集对应一类动态仿真环境，其中多台客户端车辆在城市区域内移动。每台客户端均运行流式云服务，持续接收数据包。本实验采用Simu5G库开展网络仿真，该库可在OMNeT++平台中模拟5G蜂窝网络环境。仿真器的无线参数依据国际电信联盟提出的宏蜂窝模型进行配置。仿真地图为欧洲首都郊区一块600×600平方米的城市区域。该国国家网络运营商在此区域内部署了4台5G基站（gNodeBs），形成4个5G蜂窝小区。该区域由真实道路网络划分为多个街区，我们通过OpenStreetMap（OSM）实例将该道路网络集成至仿真环境中。根据对应国家的车辆密度统计数据，本次仿真共设置25台车辆。道路网络的交通流由SUMO（Simulation of Urban MObility）工具模拟：该工具可将真实世界的OSM地图数字化，并为车辆生成路径文件。路径文件被导入Simu5G仿真器，以开展网络与车辆移动的联合仿真。针对每台车辆的行驶路径，我们采用SUMO针对城市环境的默认参数：指数型速度模型（其最高限速符合OSM交通规则），以及交叉口处车道保持、左转、右转的概率分别为0.5、0.25与0.25的概率矩阵。我们通过OMNeT++的监控服务为每台车辆采集以下信息：时间戳、信道质量指示符、数据包时延、实测信噪比（SNR）、客户端位置（x,y,z）、客户端速度（x,y,z）、接收信噪比、无线链路控制吞吐量、服务小区、客户端吞吐量。上述特征以1Hz的频率进行采样，共同构成本合成时序服务质量（QoS）数据集。我们额外构建了两类对应于仿真环境中长期显著变化的互补漂移数据集：1）网络基础设施驱动场景（Sc1）；2）人类行为驱动场景（Sc2）。在Sc1中，我们假设出于降本运维策略或移动网络运营商（MNOs）采用的基础设施共享方案，4台gNodeBs中的2台被关停。在Sc2中，我们修改用户的移动模式：假设在地图右下角区域新增一处“热点”（例如地铁站），导致该区域的交通流量提升，具体实现方式为在SUMO的路径规划中增加通往该“热点”的路径生成概率。所有生成的数据集总仿真时长均为20小时，且漂移事件均在t=10小时处引入。