Energy-Efficient Distributed Machine Learning in Cloud Fog Networks

TL;DR

This paper proposes a distributed machine learning framework in cloud-fog networks to reduce energy consumption and latency by processing DNN layers across IoT devices, fog servers, and cloud data centers.

Contribution

It introduces a MILP-based optimization model for energy-efficient DNN layer allocation across cloud and fog nodes, enhancing IoT data processing.

Findings

Distributed processing reduces energy consumption compared to centralized cloud processing.

Optimized layer allocation improves latency and QoS in IoT networks.

Input distribution impacts the overall performance of the distributed ML system.

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).