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To circumvent persistent connectivity to the cloud infrastructure, thecurrent emphasis on computing at network edge devices in the multi-robot domainis a promising enabler for delay-sensitive jobs, yet its adoption is rife withchallenges. This paper proposes a novel utility-aware dynamic task offloadingstrategy based on a multi-edge-robot system that takes into accountcomputation, communication, and task execution load to minimize the overallservice time for delay-sensitive applications. Prior to task offloading,continuous device, network, and task profiling are performed, and for each taskassigned, an edge with maximum utility is derived using a weighted utilitymaximization technique, and a system reward assignment for task connectivity orsensitivity is performed. A scheduler is in charge of task assignment, whereasan executor is responsible for task offloading on edge devices. Experimentalcomparisons of the proposed approach with conventional offloading methodsindicate better performance in terms of optimizing resource utilization andminimizing task latency.

Utility AI for Dynamic Task Offloading in the Multi-Edge Infrastructure