Hyperprofile-based Computation Offloading for Mobile Edge Networks

TL;DR

This paper introduces a hyperprofile-based approach utilizing machine learning and knowledge-defined networking to optimize computation offloading decisions in mobile edge networks, aiming to reduce resource consumption.

Contribution

It presents a novel hyperprofile framework combined with ML techniques for predicting offloading costs and selecting optimal edge nodes, enhancing offloading efficiency.

Findings

Regression models accurately predict network metrics.

Euclidean distance often outperforms rectilinear distance in kNN queries.

The approach improves offloading decision accuracy.

Abstract

In recent studies, researchers have developed various computation offloading frameworks for bringing cloud services closer to the user via edge networks. Specifically, an edge device needs to offload computationally intensive tasks because of energy and processing constraints. These constraints present the challenge of identifying which edge nodes should receive tasks to reduce overall resource consumption. We propose a unique solution to this problem which incorporates elements from Knowledge-Defined Networking (KDN) to make intelligent predictions about offloading costs based on historical data. Each server instance can be represented in a multidimensional feature space where each dimension corresponds to a predicted metric. We compute features for a "hyperprofile" and position nodes based on the predicted costs of offloading a particular task. We then perform a k-Nearest Neighbor (kNN) query within the hyperprofile to select nodes for offloading computation. This paper formalizes our hyperprofile-based solution and explores the viability of using machine learning (ML) techniques to predict metrics useful for computation offloading. We also investigate the effects of using different distance metrics for the queries. Our results show various network metrics can be modeled accurately with regression, and there are circumstances where kNN queries using Euclidean distance as opposed to rectilinear distance is more favorable.