systems administration

In cloud environments, selecting optimal data protection strategies for business continuity and disaster recovery remains challenging due to the lack of quantitative foundations for evaluating reliability and aligning with organizational Recovery Time Objectives (RTOs) and operational requirements. Method: This paper proposes an integrated assessment framework that synergistically combines system dynamics modeling and simulation-based optimization. It quantitatively evaluates key performance indicators—including recovery timeliness, data integrity, and system robustness—across public and hybrid cloud scenarios by simulating mainstream recovery mechanisms. Contribution/Results: The framework innovatively applies system dynamics to model time-varying dependencies during recovery processes and establishes interpretable, traceable mappings between policy parameters, technical metrics, and business objectives. Empirical validation demonstrates its reproducibility and practical utility, providing cloud-native organizations with a quantifiable, verifiable, and actionable decision-support methodology for data protection strategy selection.

Configuration discrepancies between software development and production environments frequently cause behavioral inconsistencies, recurrent failures, and unplanned downtime. To address this, we conducted in-depth interviews with 17 industry experts and applied thematic analysis to systematically identify key pain points in configuration governance. Based on empirical evidence, we propose the first structured, practice-oriented taxonomy of mitigation strategies—comprising eight actionable categories: process design, automated deployment, Infrastructure-as-Code (IaC) adoption, Docker-based containerized validation, virtualization-based environment isolation, and closed-loop verification, among others. This taxonomy bridges a critical methodological gap in industrial configuration drift management, significantly enhancing environment consistency, incident response efficiency, and regulatory compliance assurance. The framework has been successfully implemented and validated across multiple enterprise DevOps pipelines.

This paper addresses the conceptual ambiguity, ill-defined boundaries, and lack of implementation standards between Infrastructure-as-Code (IaC) and Pipeline-as-Code in DevOps practice. To resolve these issues, we systematically delineate their respective roles and synergistic mechanisms within the DevOps ecosystem and propose a reusable, standardized IaC-driven CI/CD implementation framework. Our approach integrates Terraform for infrastructure provisioning, Ansible for configuration management, GitLab CI for pipeline orchestration, and Docker/Kubernetes for containerized deployment—enabling an end-to-end automated delivery pipeline. Empirical evaluation demonstrates 99.8% configuration change accuracy, reduces environment provisioning time from hours to minutes, and significantly improves deployment consistency and delivery efficiency.

This paper addresses the fragmentation of information security governance and insufficient standard alignment in cloud computing environments. Methodologically, it proposes a layered security operations reference framework that integrates cloud service models (IaaS/PaaS/SaaS) with mainstream security frameworks (ISO/IEC 27001 and the OWASP Cloud Security Guidelines), and—novelty—systematically unifies cloud-native architectural characteristics with Secure SDLC practices to establish an actionable security governance pathway across public cloud, private cloud, and all three cloud service layers. The core contribution is a structured cloud security operations checklist and a control-mapping matrix that jointly aligns regulatory compliance requirements and risk mitigation objectives. This dual-dimension alignment significantly enhances security controllability and accelerates standards implementation during cloud migration.

This work addresses the gap in current systems education, where learning resources often consist of superficial tutorials or AI-generated summaries that inadequately convey foundational design principles and thus fail to cultivate robust engineering capabilities. To remedy this, we propose a structured learning pathway centered on seminal research papers from distributed systems, operating systems, and big data domains. Integrating insights from leading academic curricula and industry practices, our approach emphasizes technical depth and problem-solving reasoning. By engaging learners in close reading of original literature, critical analysis of architectural trade-offs, and cross-domain synthesis, the framework fosters a deep understanding of underlying mechanisms and cultivates systems thinking—thereby equipping practitioners to effectively tackle complex engineering challenges and progress toward professional-level systems expertise.

This study addresses the lack of systematic guidance for enterprise software teams in choosing between monolithic and microservices architectures. The work proposes a decision-making framework that integrates technical and organizational factors, evaluating the trade-offs of each architecture across dimensions such as scalability, reliability, deployment efficiency, and organizational complexity. The assessment is grounded in system scale, business requirements, operational maturity, and long-term maintainability. Through architectural pattern analysis, a structured evaluation model, and multiple case studies, the authors develop a practical selection methodology tailored to real-world engineering contexts. This approach offers enterprises clear architectural evolution pathways and actionable guidelines aligned with their developmental stages, thereby significantly enhancing the rationality and sustainability of system design decisions.

Current cybersecurity exercise scenarios suffer from limited scalability, insufficient diversity, and inadequate fidelity to real-world enterprise IT environments, thereby constraining the development of practical skills for both human experts and AI agents. This work proposes an automated approach that integrates system modeling, procedural content generation, and virtualization techniques to enable, for the first time, the generation of large-scale, multidimensionally configurable exercise scenarios—spanning scale, scope, difficulty, complexity, and diversity. The project releases an open-source simulation platform alongside a dataset comprising one hundred thousand scenario instances, significantly enhancing training coverage and scalability.

This study investigates the similarities and differences between DevOps specialists and general software developers with respect to tool usage, technical preferences, career stage, and work arrangements, leveraging the Stack Overflow 2023 Developer Survey dataset. Employing Python (Pandas) for large-scale data cleaning and statistical analysis, the research reveals substantial overlap in the adoption of critical tools such as Docker and Kubernetes. However, DevOps practitioners are predominantly mid-career professionals, whereas general developers tend to be younger. Both groups exhibit widespread adoption of remote and hybrid work models. By providing empirical insights into the evolving roles and collaborative dynamics between these two developer archetypes, this work addresses a notable gap in the literature and underscores their complementary relationship and growing synergy within modern software development ecosystems.