Proactive Tasks Management based on a Deep Learning Model

TL;DR

This paper introduces a proactive task management system for edge computing that uses deep learning to predict demand and optimize task allocation, improving efficiency in IoT environments.

Contribution

It presents a novel demand-based task offloading model utilizing LSTM networks to forecast future interest and enhance decision-making in edge computing resource management.

Findings

The model accurately predicts task demand trends.

It improves task allocation efficiency in simulated environments.

The approach reduces unnecessary task offloading.

Abstract

Pervasive computing applications deal with intelligence surrounding users that can facilitate their activities. This intelligence is provided in the form of software components incorporated in embedded systems or devices in close distance with end users.One example infrastructure that can host intelligent pervasive services is the Edge Computing (EC) infrastructure. EC nodes can execute a number of tasks for data collected by devices present in the Internet of Things (IoT) infrastructure. In this paper, we propose an intelligent, proactive tasks management model based on the demand. Demand depicts the number of users or applications interested in using the available tasks in EC nodes, thus, characterizing their popularity. We rely on a Deep Machine Learning (DML) model and more specifically on a Long Short Term Memory (LSTM) network to learn the distribution of demand indicators for each task and estimate the future interest. This information is combined with historical observations and support a decision making scheme to conclude which tasks will be offloaded due to limited interest on them. We have to notice that in our decision making, we also take into consideration the load that every task may add to the processing node where it will be allocated. The description of our model is accompanied by a large set of experimental simulations for evaluating the proposed mechanism. We provide numerical results and reveal that the proposed scheme is capable of deciding on the fly while concluding the most efficient allocation.