High Availability Applications Research Articles

Comprehensive Security for IoT Devices with Kubernetes and Raspberry Pi Cluster

Environmental monitoring systems have gained prominence across diverse applications, necessitating the integration of cutting-edge technologies. This article comprehensively explores such a system, emphasizing the integration of a Raspberry Pi cluster with the BME680 environmental sensor within a Kubernetes framework. This study encompasses the technical aspects of hardware configuration and places a significant focus on security benchmarks and robustness validation. The environmental monitoring infrastructure discussed in this article delves into the intricacies of the Raspberry Pi cluster’s hardware setup, including considerations for scalability and redundancy. This research addresses critical security gaps in contemporary environmental monitoring systems, particularly vulnerabilities linked to IoT deployments. Amidst increasing threats, this study introduces a robust framework that integrates advanced security tools—HashiCorp (San Francisco, CA, USA) Vault v1.16 for dynamic secret management and OpenID Connect for authentication processes—to enhance applications and system integrity and resilience within the Kubernetes environment. The approach involves a multi-layered security architecture that fortifies the storage and management of credentials and ensures authenticated and authorized interactions within IoT networks. Furthermore, our research incorporates a series of security benchmark tests, including vulnerability scanning, penetration testing, and access control assessments. Additionally, this article addresses crucial aspects related to data management and analysis, detailing the methodologies employed for storing, processing, and deriving insights from the collected environmental data. It further explores the integration of the monitoring system with existing infrastructure and systems, facilitating seamless data sharing and interoperability and offering valuable insights into the system’s ability to withstand potential threats and vulnerabilities. The integration of Raspberry Pi clusters with BME680 environmental sensors within a Kubernetes-managed framework significantly enhances the scalability and security of IoT systems. This study quantifies the improvements, demonstrating at least a 30% enhancement in system responsiveness and a minimum 40% reduction in vulnerability exposures, as verified by extensive security benchmarks, including penetration testing. These advancements facilitate robust, scalable IoT deployments, with potential applications extending beyond environmental monitoring to include industrial and urban settings. The incorporation of dynamic secret management with HashiCorp Vault and secure authentication with OpenID Connect provides a blueprint for developing resilient IoT architectures capable of supporting high-security and high-availability applications. In conclusion, this article contributes to the expanding body of knowledge in IoT and environmental monitoring and establishes a strong foundation for future work. These outcomes suggest promising directions for further research in secure IoT applications and present practical implications for the deployment of secure and scalable IoT solutions in critical infrastructures.

DSM perspective: another point of view

Distributed memory computers (DSMs) have arrived (G. Bell, 1992; 1996) to challenge mainframes. DSMs scale to 128 processors with two to eight processor nodes. As memory multiprocessors (SMPs), DSMs provide a single system image and maintain a shared everything model. Large scale UNIX servers using the SMP architecture challenge mainframes in legacy use and applications. These have up to 64 processors and a more uniform memory access. In contrast, clusters both complement and compete with SMPs and DSMs, using a shared nothing model. Clusters built from commodity computers, switches, and operating system scale to almost arbitrary sizes at lower cost while trading off SMPs single system image. Clusters are required for high availability applications. Highest performance scientific computers use the cluster (or MPP) approach. High growth markets, e.g., Internet servers, online transmission processing (OLTP), and database systems can all use clusters. The mainframe future of DSM may be questionable because: small SMPs are not as cost effective unless built from commodity components; large SMPs can be built without the DSM approach; and clusters are a cost effective alternative for most applications to SMPs, including DSMs for a wide scaling range. Nevertheless, commercial DSMs are being introduced that compete with SMPs over a broad range.