Computational Offloading in FOG computing using Machine Learning Approaches

Abstract

Computation offloading is a prominent exposition for the mobile devices that lack the computational power to execute applications that require a high computational cost. There are several criteria on which computational offloading can be performed. The common measures’ being load harmonizing at the servers on which task is to be computed, energy management, security and privacy of tasks to be offloaded and the most important being the computational requirement of the task. That being said more and more solutions for offloading use various machine learning (ML) and deep learning (DL) algorithms for predicting the best nodes off to which task is to be offloaded improving the performance of offloading by reducing the delay in computing the tasks. We present various computational offloading techniques which use ML and DL. Also, we describe numerous middleware technologies and the criteria's that are crucial for offloading in specific developments.