Deterministic Task Offloading and Resource Allocation in the IoT-Edge-Cloud Continuum

2026-04-17

AI Summary

This study addresses the urgent need for deterministic service guarantees for time-sensitive critical tasks in the IoT-edge-cloud continuum. Moving beyond conventional paradigms that prioritize minimizing latency, this work proposes a deadline-aware deterministic task offloading and resource allocation approach that treats meeting task deadlines as the primary objective. By jointly optimizing communication and computation resources and dynamically leveraging inter-task delay tolerances, the proposed method significantly enhances system throughput, task completion rate, and resource utilization efficiency under stringent resource constraints. Consequently, it strengthens both the service determinism and scalability required by time-sensitive vertical applications.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Future cellular networks will sustainably integrate computing, intelligence and services within a network of networks ecosystem that includes IoT devices and subnetworks for local communications and distributed processing. This integration creates an IoT-edge-cloud continuum that enables opportunistic task offloading across the continuum, enhancing network performance, reducing response times and allowing a flexible resource allocation that can facilitate the system to scale according to demand. Future networks should also natively support deterministic service levels for critical and time-sensitive vertical applications. In this paper, we propose a deterministic task offloading and resource allocation scheme for the joint management of communication and computing resources in the IoT-edge-cloud continuum. The proposed scheme prioritizes task completion before deadlines over minimizing the latency in the execution of individual tasks. The scheme leverages flexible latencies across tasks to support a higher number of tasks through a more efficient management of computing and communication resources that better adapts to scenarios with constrained resources.

Problem

• deterministic task offloading
• resource allocation
• IoT-edge-cloud continuum
• time-sensitive applications
• deadline-constrained tasks

Innovation

• deterministic task offloading
• resource allocation
• IoT-edge-cloud continuum
• deadline-aware scheduling
• communication-computing co-management