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

TL;DR

This paper proposes a deterministic task offloading and resource allocation scheme for IoT-edge-cloud networks, prioritizing task deadlines and efficient resource management to improve performance and scalability.

Contribution

It introduces a novel scheme that manages communication and computing resources to meet deadlines in the IoT-edge-cloud continuum.

Findings

Supports higher task volumes by prioritizing deadlines.

Enhances network performance and scalability.

Efficiently manages constrained resources.

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.