Execution Timing Control for Deterministic Task Offloading in the IoT-Edge-Cloud Continuum

2026-08-01

AI Summary

Existing task offloading approaches typically determine only the execution location while neglecting the start timing, often leading to transient congestion and failing to meet deadlines for delay-sensitive IoT applications. This work proposes a deterministic offloading mechanism that jointly optimizes both execution location and start timing, incorporating scheduling time into offloading decisions for the first time. By leveraging task slack time, the method enables spatiotemporal co-scheduling across the IoT–edge–cloud continuum through delay-budget-based scheduling, joint spatiotemporal resource optimization, and deterministic service guarantees. Experimental results demonstrate substantial performance improvements: deadline satisfaction rates increase by up to 70%, communication overhead is reduced by 40%, peak computational load decreases by 15%, and average execution time is shortened by as much as 77%.

Abstract

Latency-critical IoT applications, such as autonomous mobility and industrial automation, require deterministic guarantees to ensure that tasks are completed within strict deadlines. The 6G-enabled IoT-edge-cloud continuum can support such requirements by leveraging communication, computation and intelligence resources across devices, edge, and cloud infrastructures. However, existing task offloading strategies mainly focus on selecting where tasks are executed and typically assume immediate processing upon task arrival. This leads to transient congestion when multiple tasks coincide in time and results in inefficient resource utilization under dynamic workloads. This paper addresses these limitations by introducing an execution timing control strategy for deterministic task offloading that jointly determines where tasks are executed and when their execution starts, while guaranteeing deadline compliance. The key idea is to exploit the latency budget of tasks to control their execution timing, enabling a more balanced distribution of workload over time and reducing peak congestion across the continuum. Evaluation results show that, compared to existing benchmarks, the proposed approach achieves up to 70% higher satisfaction ratio, reduces the communication resources usage by 40%, lowers peak computing resource utilization by 15%, and decreases average execution time by up to 77%.

Problem

• Deterministic Task Offloading
• Execution Timing Control
• IoT-Edge-Cloud Continuum
• Deadline Compliance
• Transient Congestion

Innovation

• execution timing control
• deterministic task offloading
• IoT-edge-cloud continuum
• deadline compliance
• latency budget