Energy Efficient PON Backhaul Network for a VLC Based Fog Architecture

by   Wafaa B. M. Fadlelmula, et al.

In this paper, an energy efficient passive optical network (PON) architecture is proposed for backhaul connectivity in indoor visible light communication (VLC) systems. The proposed network is used to support a fog computing architecture allowing users with processing demands to access dedicated fog nodes and idle processing resources in other user devices within a building. The fog resources within a building complement fog nodes at higher layers of the access and metro networks and the central cloud data center. A mixed integer linear programming (MILP) model is developed to minimize the total power consumption associated with serving demands over the proposed architecture. The results show that the PON backhaul network improves the energy efficiency of fog computing by 66 compared to an architecture based on state-of-the-art Spine-and-Leaf connectivity.



page 1

page 2

page 3

page 4


Energy Efficient Fog based Healthcare Monitoring Infrastructure

Recent advances in mobile technologies and cloud computing services have...

Energy Efficient virtualization framework for 5G F-RAN

Fog radio access network (F-RAN) and virtualisation are promising techno...

Impact of Distributed Processing on Power Consumption for IoT Based Surveillance Applications

With the rapid proliferation of connected devices in the Internet of Thi...

Disaggregation for Energy Efficient Fog in Future 6G Networks

We study the benefits of adopting server disaggregation in the fog compu...

Disaggregation for Improved Efficiency in Fog Computing Era

This paper evaluates the impact of using disaggregated servers in the ne...

Energy Efficient VM Placement in a Heterogeneous Fog Computing Architecture

Recent years have witnessed a remarkable development in communication an...

PON-based connectivity for fog computing

Fog computing plays a crucial role in satisfying the requirements of del...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.