Edge-Based QoS-Aware Adaptive Task Placement: A Closed-Loop Control in Multi-Robot Systems

2026-05-30

AI Summary

This work addresses the significant degradation in Quality of Service (QoS) in multi-robot systems caused by static task offloading under network latency, jitter, and edge resource contention. To mitigate these issues, the authors propose and implement an edge-based closed-loop Adaptive Task Placement (ATP) controller that dynamically orchestrates the placement of perception tasks between local robots and edge servers. ATP employs a lightweight, multi-metric-driven mechanism, leveraging a normalized multidimensional cost function over a two-second control window to jointly evaluate communication latency, CPU utilization, and migration overhead. Implemented on a real multi-robot platform, ATP achieves QoS-aware dynamic task orchestration for the first time in such settings. Experimental results demonstrate that under computational stress and network failure scenarios, ATP substantially reduces tail latency and deadline violation rates compared to static offloading strategies, offering practical design guidelines for cloud-edge robotic deployments.

Abstract

Multi-robot systems (MRS) increasingly offload compute-intensive perception tasks to edge nodes to meet strict time-sensitive Quality-of-Service (QoS) constraints. However, static task orchestration on a shared edge node can severely degrade QoS due to network latency, jitter, and edge-resource contention. We present a pilot edge-centric MRS testbed using Raspberry Pi nodes to evaluate a camera-to-manipulator pipeline under three modes: local execution, static offloading, and a QoS-aware Adaptive Task Placement (ATP) controller. ATP scores candidate placements using a multi-metric cost (normalized latency, CPU utilization, and switching overhead) over two-second control windows. The closed-loop visual servoing testbed is instrumented with sub-millisecond clock synchronization, network emulation, and detailed monitoring of multiple metrics across nodes to capture realistic jitter. Experimental results under compute-stress and network-fault scenarios show that static edge offloading reduces on-board CPU load but amplifies tail latency and deadline misses. In contrast, the QoS-aware ATP controller, by switching task placement based on measured latency and utilization thresholds, consistently lowers deadline violations and tail latency. Overall, the results position ATP as a practical edge-side control primitive for MRS and concrete design guidelines for Cloud-Edge Robotics deployments within the broader cloud-fog automation, while motivating QoS-aware multi-objective workload orchestration for industrial cyber-physical systems.

Problem

• Multi-robot systems
• Edge computing
• Quality-of-Service
• Task offloading
• Latency

Innovation

• Adaptive Task Placement
• Edge Computing
• Quality-of-Service (QoS)
• Multi-Robot Systems
• Closed-Loop Control