Energy-Efficient Distributed Machine Learning in Cloud Fog Networks

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 centraliz...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-05
Main Authors: Alenazi, Mohammed M, Yosuf, Barzan A, Mohamed, Sanaa H, El-Gorashi, Taisir E H, Elmirghani, Jaafar M H
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Data centers
Design optimization
Energy efficiency
Integer programming
Internet of Things
Linear programming
Machine learning
Mixed integer
Network latency
Power consumption
Quality of service architectures
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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).
ISSN:2331-8422