Machine learning for cloud, fog, edge and serverless computing environments: comparisons, performance evaluation benchmark and future directions. (20th August 2022)

• Record Type: Journal Article
• Authors: Singh, Parminder; Kaur, Avinash; Gill, Sukhpal Singh
• Is Part Of: International journal of grid and utility computing. Volume 13:Number 4(2022)
• Page Start: 447
• Page End: 457
• Publication Date: 2022-08-20
• Subjects: artificial intelligence -- fog computing -- edge computing -- internet-of-things -- machine learning -- serverless computing -- cloud computing; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

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.