An Intelligent Edge-Centric Queries Allocation Scheme based on Ensemble Models

TL;DR

This paper introduces a novel meta-ensemble learning scheme to optimize the allocation of IoT data processing queries to edge computing nodes, aiming to reduce latency and improve decision accuracy.

Contribution

It presents a new ensemble-based decision model for efficient query-to-node allocation in IoT-edge environments, enhancing performance over existing methods.

Findings

The proposed scheme improves query allocation accuracy.

Experimental results show reduced latency in query processing.

The model effectively adapts to different IoT scenarios.

Abstract

The combination of Internet of Things (IoT) and Edge Computing (EC) can assist in the delivery of novel applications that will facilitate end users activities. Data collected by numerous devices present in the IoT infrastructure can be hosted into a set of EC nodes becoming the subject of processing tasks for the provision of analytics. Analytics are derived as the result of various queries defined by end users or applications. Such queries can be executed in the available EC nodes to limit the latency in the provision of responses. In this paper, we propose a meta-ensemble learning scheme that supports the decision making for the allocation of queries to the appropriate EC nodes. Our learning model decides over queries' and nodes' characteristics. We provide the description of a matching process between queries and nodes after concluding the contextual information for each envisioned characteristic adopted in our meta-ensemble scheme. We rely on widely known ensemble models, combine them and offer an additional processing layer to increase the performance. The aim is to result a subset of EC nodes that will host each incoming query. Apart from the description of the proposed model, we report on its evaluation and the corresponding results. Through a large set of experiments and a numerical analysis, we aim at revealing the pros and cons of the proposed scheme.