## 1 Introduction  
Software development challenges have arisen due to increases in IoT device-as-a-service, IoT infrastructure-as-a-service, and edge infrastructure-as-a-service [1]. In IoT, smart sensing and actuating objects are coupled with information and communication technologies (ICT). They are embedded in digital environments to produce huge volumes of data. These data need to be extracted, processed, and analyzed efficiently to arrive at meaningful information to support IoT applications. The implementations are done using different platforms as services. The applicability levels of these platforms are challenged on their efficiency, performance, and variating scalabilities. This occurs in case of a system escalation, along with data analytics and resource consumption--commonly characteristics of monolithic applications [2]. A monolithic application is defined as a single-tiered software application that integrates users' interfaces and data access codes as a single program within the same platform. Nowadays, IoT devices contain embedded networking stacks, connecting directly to both publisher and subscriber service providers. The health and fire prevention domains are particularly related to the IoT reference architectures, focusing on sensors, network aspects, applications, and presentation aspects, including data analytics. IoT analytics are evolving with newer technologies, tools, and data processing, along with analytic methods. This hasled to enhancements in the development of environments, with increased productivity and improved efficiency. However, there is no ownership in data management, and services escalate out of control if service charges are not clear. The other issue is the implementation of an IoT information system, which requires a high-level holistic approach. That approach must mix traditional data collection from vendor-specific cloud back ends with embedded hardware and mobile devices in real-time. IoT systems provide important, sometimes critical services, requiring uninterrupted operation or high levels of availability. Additionally, their complexity makes them difficult to integrate, while also complicating in-depth monitoring to prevent anomalies and failures [3]. The DevOps methodology maintains system characteristics, such as availability, scalability, and fault tolerance, on a live system [4; 5; 6; 7; 8]. The continuously rapid integration of new devices with feedback acquisition to instrument maintenance and updates leads to job accumulation. This methodology requires a multidisciplinary collaboration between development and operations teams to facilitate customer satisfaction. The collaborative team must test the platform in production, and build new releases to deploy them [9]. DevOps is a solution to facilitate that collaboration by accelerating and improving the cycles of maintenance [10; 11; 12]. The motivation of this research was to contribute to the improvement of building safety prevention (specifically, from fire outbreaks) using IoT technologies. The main question was how to integrate various IoT sensor nodes with scalable characteristics to provide a sound IoT system that provided real-time communication to the stakeholders (fire brigades) in case of a fire outbreak. The idea arose due to various challenges, such as IoT applications' scalability, updatability, integration, and maintainability that affect performance and efficiency. This research was aimed at designing and implementing an IoT framework architecture that supported multiple concurrent deployed IoT applications in a smart building. The main objective was to develop a containerized architecture performance analysis for the IoT framework based on enhanced fire prevention, tested in Rwanda.