A Proactive Uncertainty driven Model for Tasks Offloading

TL;DR

This paper presents a proactive, fuzzy system-based model for IoT node task offloading that prevents overloads, prioritizes high-demand tasks, and ensures fast, uninterrupted service through a self-healing mechanism.

Contribution

It introduces a novel proactive, self-healing model combining fuzzy systems and non-parametric statistics to manage IoT node traffic effectively.

Findings

The model prevents node overloading under heavy traffic.

High priority and demand tasks are served without interruption.

Experimental results demonstrate improved performance and response times.

Abstract

The ever-increasing demands of end-users on the Internet of Things (IoT), often cause great congestion in the nodes that serve their requests. Therefore, the problem of node overloading arises. In this article we attempt to solve the problem of heavy traffic in a node, by proposing a mechanism that keeps the node from overloading, regardless of the load entering in it, and which takes into consideration both the priority and the task demand. More specifically, we introduce a proactive, self-healing mechanism that utilizes fuzzy systems, in combination to a non-parametric statistic method. Through our approach, we manage to ensure the uninterrupted service of high demand or priority tasks, regardless of the load the node may receive, based on a proactive approach. Also, we ensure the fastest possible result delivery to the requestors, through the high priority and high demand sensitive mechanism. A series of experimental scenarios are used to evaluate the performance of the suggested model, while we present the relevant numerical results.