Lookup NU author(s): Dr Tejal Shah,
Professor Raj Ranjan
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2017 Elsevier B.V. In the past few years, several studies proposed to reduce the impact of bushfires by mapping their occurrences and spread. Most of these prediction/mapping tools and models were designed to run either on a single local machine or a High performance cluster, neither of which can scale with users' needs. The process of installing these tools and models their configuration can itself be a tedious and time consuming process. Thus making them, not suitable for time constraint cyber-physical emergency systems. In this research, to improve the efficiency of the fire prediction process and make this service available to several users in a scalable and cost-effective manner, we propose a scalable Cloud based bushfire prediction framework, which allows forecasting of the probability of fire occurrences in different regions of interest. The framework automates the process of selecting particular bushfire models for specific regions and scheduling users' requests within their specified deadlines. The evaluation results show that our Cloud based bushfire prediction system can scale resources and meet user requirements.
Author(s): Garg S, Aryal J, Wang H, Shah T, Kecskemeti G, Ranjan R
Publication type: Article
Publication status: Published
Journal: Future Generation Computer Systems
Print publication date: 01/02/2018
Online publication date: 16/03/2017
Acceptance date: 06/02/2017
Date deposited: 20/08/2017
ISSN (print): 0167-739X
ISSN (electronic): 1872-7115
Publisher: Elsevier BV
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