Energy Efficient and Resilient Infrastructure for Fog Computing Health Monitoring Applications

TL;DR

This paper presents a resilient, energy-efficient fog computing infrastructure for health monitoring, optimizing server placement to minimize energy use while ensuring resilience against failures, considering geographical constraints.

Contribution

It introduces a MILP model for optimizing server placement in fog computing health applications, accounting for resilience and geographical constraints, which is a novel approach.

Findings

Geographical constraints increase energy consumption by up to 9%.

More processing servers per node reduce energy use and network load.

Optimized server placement enhances resilience and efficiency.

Abstract

In this paper, we propose a resilient energy efficient and fog computing infrastructure for health monitoring applications. We design the infrastructure to be resilient against server failures under two scenarios; without geographical constraints and with geographical constraints. We consider a heart monitoring application where patients send their 30-seconds recording of Electrocardiogram (ECG) signal for processing, analysis and decision making at both primary and backup servers. A Mixed Integer Linear Programming (MILP) model is used to optimize the number and locations of the primary and backup processing servers so that the energy consumption of both the processing and networking equipment are minimized. The results show that considering geographical constraints yields a network energy consumption increase by up to 9% compared to without geographical constraint. The results also show that, increasing the number of processing servers that can be served at each candidate node can reduce the energy consumption of networking equipment besides reducing the rate of energy increase of networking equipment due to increasing level of demand.