Energy-Efficient AI over a Virtualized Cloud Fog Network

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

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

4 Citations (Scopus)


Deep Neural Networks (DNNs) have served as a catalyst in introducing a plethora of next-generation services in the era of Internet of Things (IoT), thanks to the availability of massive amounts of data collected by the objects on the edge. Currently, DNN models are used to deliver many Artificial Intelligence (AI) services that include image and natural language processing, speech recognition, and robotics. Accordingly, such services utilize various DNN models that make it computationally intensive for deployment on the edge devices alone. Thus, most AI models are offloaded to distant cloud data centers (CDCs), which tend to consolidate large amounts of computing and storage resources into one or more CDCs. Deploying services in the CDC will inevitably lead to excessive latencies and overall increase in power consumption. Instead, fog computing allows for cloud services to be extended to the edge of the network, which allows for data processing to be performed closer to the end-user device. However, different from cloud data centers, fog nodes have limited computational power and are highly distributed in the network. In this paper, using Mixed Integer Linear Programming (MILP), we formulate the placement of DNN inference models, which is abstracted as a network embedding problem in a Cloud Fog Network (CFN) architecture, where power savings are introduced through trade-offs between processing and networking. We study the performance of the CFN architecture by comparing the energy savings when compared to the baseline approach which is the CDC.

Original languageEnglish
Title of host publicationProceedings of the 202112th ACM International Conference on Future Energy Systems
Subtitle of host publicatione-Energy 2021
EditorsHermann De Meer, Michela Meo, Omid Ardakanian, Astrid Nieße
Place of PublicationUnited States
PublisherAssociation for Computing Machinery (ACM)
Number of pages7
ISBN (Electronic)9781450383332
Publication statusPublished - 22 Jun 2021
Externally publishedYes
Event12th ACM International Conference on Future Energy Systems, e-Energy 2021 - Virtual, Online, Italy
Duration: 28 Jun 20212 Jul 2021

Publication series

Namee-Energy 2021 - Proceedings of the 2021 12th ACM International Conference on Future Energy Systems


Conference12th ACM International Conference on Future Energy Systems, e-Energy 2021
CityVirtual, Online


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