Title: Machine learning for cloud, fog, edge and serverless computing environments: comparisons, performance evaluation benchmark and future directions

Authors: Parminder Singh; Avinash Kaur; Sukhpal Singh Gill

Addresses: School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India ' School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India ' School of Electronic Engineering and Computer Science, Queen Mary University of London, London, England, UK

Abstract: The compute-intensive and latency-sensitive Internet of Things (IoT) applications need to utilise the services from various computing paradigms, but they are facing many challenges such as large value of latency, energy and network bandwidth. To analyse and understand these challenges, we designed a performance evaluation benchmark which integrates Cloud, Fog, Edge and Serverless computing to conduct a comparative study for IoT-based healthcare application. It gives the platform for the developers to design IoT applications based on user guidelines to run various applications concurrently on different paradigms. Furthermore, we used recent machine learning techniques for the optimisation of resources, energy, cost and overheads to identify the best technique based on important Quality of Service parameters. Experimental results show that serverless computing performs better than non-serverless in terms of energy, latency, bandwidth, response time and scalability by 3.8%, 3.2%, 4.3%, 1.5% and 2.7%, respectively. Finally, various promising future directions are highlighted.

Keywords: artificial intelligence; fog computing; edge computing; internet-of-things; machine learning; serverless computing; cloud computing.

DOI: 10.1504/IJGUC.2022.125151

International Journal of Grid and Utility Computing, 2022 Vol.13 No.4, pp.447 - 457

Received: 06 Jul 2021
Accepted: 29 Jul 2021
Published online: 31 Aug 2022 *

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