Alenazi, MM, Yosuf, BA, Mohamed, SH et al. (2 more authors) (2021) Energy-Efficient Distributed Machine Learning in Cloud Fog Networks. In: 2021 IEEE 7th World Forum on Internet of Things (WF-IoT). IEEE 7th World Forum on Internet of Things (WF-IoT 2021), 14 Jun - 31 Jul 2021, New Orleans, Louisiana, USA/Virtual. IEEE , pp. 935-941. ISBN 978-1-6654-4431-6
Abstract
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).
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Deep learning, Cloud computing, Data centers, Power demand, Quality of service, Energy efficiency, Internet of Things |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/H040536/1 EPSRC (Engineering and Physical Sciences Research Council) EP/K016873/1 EPSRC (Engineering and Physical Sciences Research Council) EP/S016570/1 EPSRC (Engineering and Physical Sciences Research Council) EP/K503836/1 EPSRC (Engineering and Physical Sciences Research Council) EP/R511717/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Dec 2021 14:10 |
Last Modified: | 08 Dec 2021 14:10 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/WF-IoT51360.2021.9595351 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:181241 |