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Cited by 11IoT and Edge/Fog ComputingCloud Computing and Resource ManagementEnergy Efficiency in Computing

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

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Singh, Parminder, Kaur, Avinash, Gill, Sukhpal Singh·2022·International Journal of Grid and Utility Computing
DOI 10.1504/ijguc.2022.125151
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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.

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  • In smart parking, data from sensors is transferred to edge devices efficiently and reliably.

    “For example, in smart parking scenarios where data is coming from sensors and transferred to edge devices efficiently and reliably [66].”

    From Edge AI: A survey (2023) · cites this paper as [66]

    Section 5.1 (page unavailable) describes sensor data being forwarded to gateway devices such as tablets, laptops and mobile phones; the Introduction identifies mobile phones and personal computers as potential edge devices. The copy does not establish this transfer in smart parking or say it is reliable, and section 7.1 treats reliability as a parameter for future work. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Cloudlets initiated developments in edge computing but have proved marginal in commercial practice, though they could find implementations in municipality-centred schemes based on free access and open-source software, unlike subscription or fee-based services in other edge computing paradigms.

    “Although cloudlets initiated these developments in edge computing, they have proved marginal in commercial practice but could find implementations in municipality-centred schemes based on free access and open-source software, in contrast to the subscription or fee-based services in other edge computing paradigms [66].”

    From Edge AI: A survey (2023) · cites this paper as [66]

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Cites

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Research works printed but not linked: 39

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Authors
Singh, P., Kaur, A., & Gill, S. S.
Affiliations
  • Lovely Professional University (IN)
  • Queen Mary University of London (GB)
ORCID
Venue
International Journal of Grid and Utility Computing
Publisher
Inderscience Publishers
Published
2022
Volume
13
Issue
4
Pages
447
ISSN
  • 1741-847X (ISSN-L)
  • 1741-8488
DOI
10.1504/ijguc.2022.125151
OpenAlex
W4294030689
Cited by
11
References
1
Citations by year
2026: 1 · 2025: 1 · 2024: 6 · 2023: 2 · 2022: 1
FWCI
1.11
Type
journal article
Language
en
Open access
Locations
  • International Journal of Grid and Utility Computing (publishedVersion)
  • Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
Keywords
machine learning, cloud computing, fog computing, edge computing, serverless computing, Internet of Things, resource optimization, energy efficiency, latency, quality of service, scalability

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