Energy-efficient PON-based Backhaul Connectivity for a VLC-enabled Indoor Fog Computing Environment

TL;DR

This paper proposes an energy-efficient VLC-enabled indoor fog computing architecture using a PON-based backhaul, optimized via MILP, achieving significant power savings and enhanced energy efficiency over traditional and cloud-based systems.

Contribution

It introduces a novel PON-based backhaul architecture optimized for VLC-enabled fog computing, including dynamic resource allocation and inter-building resource sharing.

Findings

Up to 82% power savings compared to spine-and-leaf networks

Energy efficiency improvements of up to 93% over centralized cloud processing

Enhanced architecture with dynamic bandwidth and inter-building resource sharing

Abstract

In this paper, we consider the use of visible light communication (VLC) to provide connectivity to indoor fog computing resources and propose an energy-efficient passive optical network (PON)-based backhaul architecture to support the VLC system. We develop a mixed-integer linear programming (MILP) model to optimize the allocation of computing resources over the proposed architecture, aiming to minimize processing and networking power consumption. We evaluate the performance of the proposed architecture under varying workload demands and user distributions. Comparative analysis against a backhaul architecture that is based on the state-of-the-art spine-and-leaf (S&L) network design demonstrates total power savings of up to 82%. Further comparison with centralized cloud processing shows improvements in energy efficiency of up to 93%. Additionally, we examine the improvements in energy efficiency obtained by splitting tasks among multiple processing nodes and propose enhancements to the architecture including dynamic bandwidth allocation, increased wavelength bandwidth and improved connectivity within rooms to alleviate networking bottlenecks. Furthermore, we introduce an inter-building architecture that leverages resources from neighboring buildings to support high-demand scenarios.