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Computational Offloading in FOG computing using Machine Learning Approaches
Author(s) -
Najmus Saqib,
Nadeem Yousuf Khanday
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit206221
Subject(s) - computer science , computation offloading , task (project management) , server , distributed computing , fog computing , middleware (distributed applications) , mobile device , edge computing , cloud computing , artificial intelligence , operating system , management , economics
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.