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

2026-04-17

AI Summary

This work addresses the joint challenge of guaranteeing stringent deterministic service levels—such as bounded latency—and achieving system scalability in the IoT-edge-cloud continuum. To this end, it proposes a deterministic task offloading and resource allocation framework tailored for 6G multi-domain integrated architectures. The approach uniquely integrates deterministic service guarantees with scalability through a deterministic scheduling mechanism, a cross-domain joint communication and computation resource allocation model, and a deadline-aware dynamic control strategy. This integration ensures bounded end-to-end latency while simultaneously promoting load balancing and efficient resource utilization. Experimental results demonstrate that the proposed method substantially outperforms existing solutions in terms of system scalability, task processing efficiency, and fairness in resource allocation.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Scalability of ML Systems

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 6 G networks are envisioned as a network of networks (NoN) ecosystem, integrating communication and computing resources across multiple domains. At the deep edge, IoT and end-user devices will form subnetworks for local communication and distributed task processing. These subnetworks will seamlessly integrate into the NoN ecosystem, creating an IoT-edge-cloud continuum. The unified resources across this continuum facilitate dynamic and scalable task offloading, unlocking new possibilities to support emerging services, including critical vertical services with stringent reliability and deterministic service level requirements. In this context, this paper demonstrates that a deterministic approach to task offloading and resource (communication and computing) allocation in the IoT-edge-cloud continuum not only ensures deterministic service levels but also enhances scalability compared to existing task offloading and resource allocation methods. By flexibly managing task completion deadlines while maintaining deterministic (i.e. bounded latency) service levels, deterministic policies achieve a more balanced workload and resource distribution across the continuum, ultimately improving scalability.

Problem

• deterministic task offloading
• resource allocation
• IoT-edge-cloud continuum
• scalability
• bounded latency

Innovation

• deterministic task offloading
• resource allocation
• IoT-edge-cloud continuum
• scalability
• bounded latency