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

TL;DR

This paper proposes a deterministic task offloading and resource allocation method in the IoT-edge-cloud continuum, ensuring deterministic service levels and improved scalability for emerging 6G network services.

Contribution

It introduces a scalable deterministic approach that manages task deadlines and resource distribution across the IoT-edge-cloud ecosystem.

Findings

Deterministic policies ensure bounded latency and reliable service levels.

The approach enhances scalability compared to existing methods.

Workload and resource distribution are more balanced with the proposed method.

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.