The advent of edge intelligence and escalating concerns for data privacy protection have sparked a surge of interest in device-cloud collaborative computing. Large-scale device deployments to validate prototype solutions are often prohibitively expensive and practically challenging, resulting in a pronounced demand for simulation tools that can emulate realworld scenarios. However, existing simulators predominantly rely solely on high-performance servers to emulate edge computing devices, overlooking (1) the discrepancies between virtual computing units and actual heterogeneous computing devices and (2) the simulation of device behaviors in real-world environments. In this paper, we propose a high-fidelity device simulation platform, called SimDC, which uses a hybrid heterogeneous resource and integrates high-performance servers and physical mobile phones. Utilizing this platform, developers can simulate numerous devices for functional testing cost-effectively and capture precise operational responses from varied real devices. To simulate real behaviors of heterogeneous devices, we offer a configurable device behavior traffic controller that dispatches results on devices to the cloud using a user-defined operation strategy. Comprehensive experiments on the public dataset show the effectiveness of our simulation platform and its great potential for application. The code is available at https://github.com/opas-lab/olearning-sim.

翻译：边缘智能的兴起及对数据隐私保护日益增长的关注，激发了设备-云协同计算的研究热潮。为验证原型解决方案而进行大规模设备部署往往成本高昂且实践困难，因此亟需能够模拟真实场景的仿真工具。然而，现有仿真器主要依赖高性能服务器模拟边缘计算设备，忽视了（1）虚拟计算单元与实际异构计算设备之间的差异，以及（2）真实环境中设备行为的仿真。本文提出一种高保真设备仿真平台SimDC，该平台采用混合异构资源架构，整合了高性能服务器与物理移动设备。利用此平台，开发者能够以低成本模拟海量设备进行功能测试，并精准捕获来自不同真实设备的运行响应。为模拟异构设备的真实行为，我们设计了可配置的设备行为流量控制器，可根据用户定义的操作策略将设备端结果分发至云端。在公开数据集上的综合实验验证了本仿真平台的有效性及其广阔的应用潜力。代码已开源：https://github.com/opas-lab/olearning-sim。