Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research
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Cloud computing is suitable for handling large volumes of data, computationally intensive tasks, or scenarios where edge computing resources may be limited.
“It is suitable for handling large volumes of data, computationally intensive tasks, or scenarios where edge computing resources may be limited [26].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [26]
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“With the advances in Deep Learning and the idea of Federated Machine Learning at the edge, conventual metrics require to be reexamined in the context of edge computing [28].”
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Self-citationA Survey of Video Surveillance Systems in Smart City
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“It is suitable for handling large volumes of data, computationally intensive tasks, or scenarios where edge computing resources may be limited [26].”
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- Authors
- Aslanpour, M. S., Gill, S. S., & Toosi, A. N.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Queen Mary University of London (GB)
- Monash University (AU)
- Corresponding author
- Gill, Sukhpal Singh
- Venue
- Internet of Things
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 12
- Pages
- 100273
- ISSN
- 2542-6605 (ISSN-L)
- OpenAlex
- W3048498988
- Cited by
- 289
- References
- 58
- Citations by year
- 2026: 27 · 2025: 53 · 2024: 70 · 2023: 54 · 2022: 45 · 2021: 37 · 2020: 3
- FWCI
- 15.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Internet of Things (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
- performance indicators, cloud computing, fog computing, edge computing, benchmarking, evaluation standards, latency, energy efficiency, resource allocation, task scheduling, resource provisioning, resource scheduling, multi-objective optimization
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