Energy-Efficient Distributed Machine Learning in Cloud Fog Networks

Mohammed M. Alenazi, Barzan A. Yosuf, Sanaa H. Mohamed, Taisir E.H. El-Gorashi, Jaafar M.H. Elmirghani

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

3 Citations (Scopus)


Massive amounts of data are expected to be generated by the billions of objects that form the Internet of Things (IoT). A variety of automated services such as monitoring will largely depend on the use of different Machine Learning (ML) algorithms. Traditionally, ML models are processed by centralized cloud data centers, where IoT readings are offloaded to the cloud via multiple networking hops in the access, metro, and core layers. This approach will inevitably lead to excessive networking power consumptions as well as Quality-of-Service (QoS) degradation such as increased latency. Instead, in this paper, we propose a distributed ML approach where the processing can take place in intermediary devices such as IoT nodes and fog servers in addition to the cloud. We abstract the ML models into Virtual Service Requests (VSRs) to represent multiple interconnected layers of a Deep Neural Network (DNN). Using Mixed Integer Linear Programming (MILP), we design an optimization model that allocates the layers of a DNN in a Cloud/Fog Network (CFN) in an energy efficient way. We evaluate the impact of DNN input distribution on the performance of the CFN and compare the energy efficiency of this approach to the baseline where all layers of DNNs are processed in the centralized Cloud Data Center (CDC).

Original languageEnglish
Title of host publication7th IEEE World Forum on Internet of Things, WF-IoT 2021
EditorsAhmed Abdelgawad, Soumya Kanti Dutta, RangaRao Venkatesha Prasad
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)9781665444316
ISBN (Print)9781665444323
Publication statusPublished - 14 Jun 2021
Externally publishedYes
Event7th IEEE World Forum on Internet of Things, WF-IoT 2021 - New Orleans, United States
Duration: 14 Jun 202131 Jul 2021

Publication series

Name7th IEEE World Forum on Internet of Things, WF-IoT 2021


Conference7th IEEE World Forum on Internet of Things, WF-IoT 2021
Country/TerritoryUnited States
CityNew Orleans


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