Pure Edge computing (PEC) aims to bring cloud applications and services to the edge of the network to support the growing user demand for time-sensitive applications and data-driven computing. However, mobility and limited computational capacity of edge devices pose challenges in supporting some urgent and computationally intensive tasks with strict response time demands. If the execution results of these tasks exceed the deadline, they become worthless and can cause severe safety issues. Therefore, it is essential to ensure that edge nodes complete as many latency-sensitive tasks as possible. \\In this paper, we propose a Robust Adaptive Workload Orchestration (R-AdWOrch) model to minimize deadline misses and data loss by using priority definition and a reallocation strategy. The results show that R-AdWOrch can minimize deadline misses of urgent tasks while minimizing the data loss of lower priority tasks under all conditions.

本文提出了一种鲁棒自适应工作负载编排（R-AdWOrch）模型，通过使用优先级定义和重新分配策略，最小化紧急任务的截止日期延迟和低优先级任务的数据丢失，结果表明R-AdWOrch在所有情况下都能最小化紧急任务的截止日期延迟，并最小化低优先级任务的数据丢失。

纯边缘计算中的鲁棒自适应工作负载编排