To circumvent persistent connectivity to the cloud infrastructure, the current emphasis on computing at network edge devices in the multi-robot domain is a promising enabler for delay-sensitive jobs, yet its adoption is rife with challenges. This paper proposes a novel utility-aware dynamic task offloading strategy based on a multi-edge-robot system that takes into account computation, communication, and task execution load to minimize the overall service time for delay-sensitive applications. Prior to task offloading, continuous device, network, and task profiling are performed, and for each task assigned, an edge with maximum utility is derived using a weighted utility maximization technique, and a system reward assignment for task connectivity or sensitivity is performed. A scheduler is in charge of task assignment, whereas an executor is responsible for task offloading on edge devices. Experimental comparisons of the proposed approach with conventional offloading methods indicate better performance in terms of optimizing resource utilization and minimizing task latency.

翻译：为规避对云基础设施的持续连接需求，当前多机器人领域对网络边缘设备计算的关注，为时延敏感型任务提供了有前景的使能技术，但其应用仍面临诸多挑战。本文提出了一种基于多边缘-机器人系统的新型效用感知动态任务卸载策略，该策略综合考虑计算、通信及任务执行负载，旨在最小化时延敏感型应用的整体服务时间。在任务卸载前，需对设备、网络及任务进行持续剖析；对于每个分配的任务，采用加权效用最大化技术推导出具有最大效用的边缘节点，并根据任务连接性或敏感性分配系统奖励。调度器负责任务分配，而执行器则负责在边缘设备上执行任务卸载。实验结果表明，与传统卸载方法相比，所提方法在优化资源利用率和最小化任务延迟方面具有更优性能。