articleIEEE Communications Surveys & TutorialsJan 1, 2023Closed access

Offloading Using Traditional Optimization and Machine Learning in Federated Cloud–Edge–Fog Systems: A Survey

BKBinayak KarWYWidhi YahyaYLYing‐Dar LinAAAsad Ali

National Taiwan University of Science and Technology · National Yang Ming Chiao Tung University · +1 more institution

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Abstract

The huge amount of data generated by the Internet of Things (IoT) devices needs the computational power and storage capacity provided by cloud, edge, and fog computing paradigms. Each of these computing paradigms has its own pros and cons. Cloud computing provides enhanced data storage and computing power but causes high communication latency. Edge and fog computing provide similar services with lower latency but limited capacity, capability, and coverage. A single computing paradigm cannot fulfill all the requirements of IoT devices and a federation between them is needed to extend their capacity, capability, and services. This federation is beneficial to both subscribers and providers and also reveals…

Citation impact

173
total citations
FWCI
33.61
Percentile
100%
References
148
Citations per year

Authors

4

Topics & keywords

Topics
Keywords
  • Computer science
  • Cloud computing
  • Fog computing
  • Edge computing
  • Latency (audio)
  • Distributed computing
  • Enhanced Data Rates for GSM Evolution
  • Data science
UN Sustainable Development Goals
  • Peace, Justice and strong institutions
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