Capacity Planning for Software Applications in Natural Disaster Scenarios

In recent years, there has been a notable increase in the development of social media and humanitarian applications based on bots, designed to provide assistance during large-scale natural disasters. These applications play a crucial role in managing the chaos and useful to satisfy the urgent needs for rescue and relief that arise when catastrophes disrupt daily routines. However, they often encounter challenges during emergencies, such as dynamic and unpredictable variations in user workload, which can affect service quality and application stability. To tackle these challenges, we propose a capacity planning methodology to determine the optimal number of replicas and partitions for each component of an application and distributes them across virtual machines in server clusters. By bridging the gap between the algorithms executed in the applications and the performance characteristics of their implementations, this methodology enables applications to scale efficiently. It helps maintain response times and average utilization within user-defined ranges while providing fault tolerance to prevent component saturation. We validate the proposed methodology with tree bot-based applications devised to be use after a natural disaster strikes. Our experimental results show the effectiveness of the methodology, with estimation errors ranging from 1% to 15% for utilization and average response times. Furthermore, the methodology serves as an effective elasticity tool, allowing for the adjustment of component replicas based on user’s requests.