The edge computing paradigm comes with a promise of lower application latency compared to the cloud. Moreover, offloading user device computations to the edge enables running demanding applications on resource-constrained mobile end devices. However, there is a lack of workload models specific to edge offloading using applications as their basis.In this work, we build upon the reconfigurable open-source mixed reality (MR) framework MR-Leo as a vehicle to study resource utilisation and quality of service for a time-critical mobile application that would have to rely on the edge to be widely deployed. We perform experiments to aid estimating the resource footprint and the generated load by MR-Leo, and propose an application model and a statistical workload model for it. The idea is that such empirically-driven models can be the basis of evaluations of edge algorithms within simulation or analytical studies.A comparison with a workload model used in a recent work shows that the computational demand of MR-Leo exhibits very different characteristics from those assumed for MR applications earlier.

中文翻译：

---

边缘计算混合现实工作负载的表征和建模

与云相比，边缘计算范式有望降低应用程序延迟。此外，将用户设备计算卸载到边缘可以在资源受限的移动终端设备上运行要求苛刻的应用程序。但是，缺乏以应用程序为基础的边缘卸载专用的工作量模型。在这项工作中，我们以可重新配置的开源混合现实（MR）框架MR-Leo为基础来研究资源利用率和质量时间紧迫的移动应用程序必须提供服务，而该应用程序必须依赖边缘才能被广泛部署。我们进行实验以帮助估计MR-Leo的资源占用量和所产生的负载，并为此提出了一个应用程序模型和一个统计工作量模型。