Efficient memory allocation is critical for controlling and optimizing High-Performance Computing (HPC) infrastructures. This action design research study aims to identify the best principles, practices, and patterns for improving the precision and efficiency of control and optimization mechanisms in large-scale HPC infrastructure. To achieve this, we explore the use of Artificial Intelligence Operations (AIOps), which applies artificial intelligence to manage software infrastructure operations. In this context, we propose Job Prophet (JP), a machine-learning-based application. JP is designed to regulate and continuously optimize HPC resource management, enhancing operational efficiency and performance. The tool integrates continuous training, inference, monitoring, and CI/CD pipelines, ensuring adaptability to dynamic workloads. By leveraging historical execution data from heterogeneous sources, JP enhances scheduling efficiency while maintaining model reliability through continuous retraining and monitoring. We evaluate JP on real-world HPC workloads, demonstrating significant improvements in resource utilization and job performance. Our findings highlight the benefits of AI-driven memory allocation and provide insights into scalable, automated machine-learning integration within HPC environments.

Chaos Engineering (CE) has emerged as a proactive method to improve the resilience of modern distributed systems, particularly within DevOps environments. Originally pioneered by Netflix, CE simulates real-world failures to expose weaknesses before they impact production. In this paper, we present a systematic gray literature review that investigates how industry practitioners have adopted and adapted CE principles over recent years. Analyzing 50 sources published between 2019 and early 2024, we developed a comprehensive classification framework that extends the foundational CE principles into ten distinct concepts. Our study reveals that while the core tenets of CE remain influential, practitioners increasingly emphasize controlled experimentation, automation, and risk mitigation strategies to align with the demands of agile and continuously evolving DevOps pipelines. Our results enhance the understanding of how CE is intended and implemented in practice, and offer guidance for future research and industrial applications aimed at improving system robustness in dynamic production environments.

A data catalog enables an organization to maintain an inventory of its data assets by collecting and managing the relevant metadata. We conducted a systematic multi-vocal literature review on data catalogs to understand their features and usage. We systematically selected and analyzed 86 literature sources and 39 catalog tools. We first utilized the findings from the literature to develop a classification framework comprising 24 fine-grained and five high-level features, along with three maturity levels. Next, we analyzed 39 tools based on the classification framework. Organizations typically include a data catalog as a component in their big data platforms and use it to support the various phases of the metadata management lifecycle. Hence, we also mapped the catalog features to the requirements of metadata-driven big data architectures, namely data mesh, data lake, and data lakehouse. Moreover, the mappings of the features to the phases in a metadata management lifecycle were developed. Our findings shall aid organizations in making informed decisions when choosing data catalog tools and help researchers identify the critical research issues in data cataloging and metadata management. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board .