Innovative Cost-Efficient Cloud Computing-Based Models for Disasters Management

Abstract

Cloud computing has transformed the digital landscape, offering scalable services to individuals and businesses. However, ensuring continuous cloud service availability requires robust disaster management. For instance, in case of strikes of natural disasters, a fully functioning cloud landscape will collapse, which leads to substantial loss in terms of time, effort, and monetary aspects. This research paper explores current cloud computing solutions, emphasizing the importance of disaster management, and introduces two innovative models for selecting potential backup sites. The study begins with a comprehensive review of existing cloud computing solutions and their disaster management mechanisms. Evaluating their strengths and limitations, However, the current disaster recovery (DR) solutions are costly since they demand permanent contracts with cloud providers to pre-assign constant DR locations as replicas of the primary landscapes. To minimize this cost, we stress the urgent need for cost-effective disaster recovery strategies. This is accomplished by developing two models considering the most influential factors that contribute to DR site selection. The Weighted Grid Decision Model (WGDM) combines geographical and environmental attributes to evaluate the desirability of candidate sites. This structured approach allows for informed decision-making. The second model, the Artificial Neural Network (ANN) model, leverages machine learning to analyse historical data on disaster incidents and their effects on cloud infrastructure. By identifying patterns and trends, the ANN model assists in making intelligent backup site choices. This research demonstrates the benefits of employing AI-driven decision-making tools for disaster management in cloud computing.