Authors: Felix Corvin

Abstract: In the modern era of digital infrastructure, organizations are increasingly adopting Infrastructure as Code (IaC) to manage and automate the provisioning and configuration of resources across both on-premises and cloud environments. IaC ensures consistency, repeatability, and efficiency by allowing infrastructure to be defined and maintained through version-controlled code. Among the many tools available, Puppet and Ansible have emerged as two of the most widely adopted solutions, each bringing distinct advantages to the automation landscape. Puppet is based on a declarative model and is particularly suited for policy enforcement and large-scale system state management. Ansible, by contrast, follows a procedural model and is known for its flexibility, simplicity, and agentless operation. This review examines the rationale, architecture, and best practices behind co-deploying Puppet and Ansible within hybrid environments. Rather than viewing these tools as mutually exclusive, the paper explores how they can be used in complementary roles to achieve higher degrees of automation maturity, compliance, and infrastructure resilience. The review discusses how Puppet can handle base operating system configurations and enforce long-term system states, while Ansible is better suited for orchestration tasks, application deployment, and change management.

DOI: https://doi.org/10.5281/zenodo.15804110

Published by: vikaspatanker

Authors: Kael Veridian

Abstract: The demand for resilient virtual machine (VM) architectures has grown exponentially with the adoption of cloud computing across enterprise sectors. Ensuring continuity of services in the face of infrastructure failures, cyber threats, and unpredictable workloads requires a robust, automated, and secure cloud environment. This review article presents a comprehensive analysis of how Red Hat’s technology stack—including Red Hat Enterprise Linux (RHEL), Kernel-based Virtual Machine (KVM), OpenStack, OpenShift, Ansible Automation Platform, and Red Hat Satellite—enables the design and deployment of resilient VM infrastructures in public, private, and hybrid cloud environments. The paper begins by outlining the foundational elements of Red Hat’s ecosystem and its integration into virtualization and cloud orchestration platforms. It then explores architectural design principles for fault tolerance, high availability, and elastic scalability, including clustering solutions using Pacemaker/Corosync and automated lifecycle management through Ansible and CloudForms. Red Hat’s support for secure VM configurations, enabled by SELinux, SCAP compliance, and FIPS-certified modules, is discussed as a critical pillar of operational resilience. The review categorizes common resiliency patterns such as active-active clustering, multi-region redundancy, and hybrid cloud deployments that leverage Red Hat Cloud Access and Image Builder. It further evaluates storage and data protection strategies through Ceph, GlusterFS, LVM snapshots, and integration with backup solutions like Veeam and Commvault. Observability and monitoring capabilities are addressed through Red Hat Performance Co-Pilot, Prometheus/Grafana, and centralized logging via EFK stacks. Several real-world case studies are presented from finance, healthcare, and government sectors to illustrate the deployment of Red Hat-based resilient VM infrastructures in production environments. The article concludes by identifying emerging trends, including AI-driven self-healing automation, serverless VM workloads via KubeVirt and MicroShift, and zero-trust security architectures powered by service mesh and mTLS. While challenges such as cross-cloud compatibility and ecosystem complexity persist, Red Hat’s comprehensive, open-source platform offers a strategic foundation for building scalable, fault-tolerant, and secure virtual infrastructures in cloud-native ecosystems.

DOI: https://doi.org/10.5281/zenodo.15804019

Published by: vikaspatanker

Authors: Ariane Solis

Abstract: The ongoing reliance on legacy UNIX systems such as Solaris, AIX, and HP-UX in mission-critical enterprise environments poses significant challenges to maintaining high availability (HA) as these platforms age. Traditional HA clustering techniques—rooted in physical infrastructure, proprietary clustering software, and tightly coupled storage systems—struggle to adapt to the elasticity, fault tolerance, and operational flexibility offered by cloud environments like Amazon Web Services (AWS). This review explores the architectural shift from legacy on-premises HA clusters to AWS-based and hybrid high availability designs for UNIX workloads. It evaluates key AWS services such as EC2, Elastic Load Balancer (ELB), CloudWatch, Auto Scaling, and Route 53 in building redundant and failover-capable environments tailored for UNIX applications. The paper highlights the challenges of migrating UNIX workloads to AWS, including hardware-bound licensing, kernel-level dependencies, shared storage constraints, and clustering heartbeat mechanisms. Strategies for bridging these limitations—through hybrid models, emulation platforms (e.g., Charon-SSP for Solaris and AIX), and containerized service proxies—are analyzed. Key components of AWS-native HA design are reviewed, including EC2 auto-recovery, cross-AZ EBS, elastic IP remapping, and application-aware health monitoring via CloudWatch and Lambda functions. Hybrid clustering configurations linking on-prem systems to AWS—emerge as transitional models, allowing legacy workloads to benefit from cloud-based failover and storage resiliency while maintaining control over core services. The review includes real-world case studies across finance, healthcare, and manufacturing that demonstrate the feasibility and impact of AWS-based HA clustering for UNIX systems. It concludes with a comparative analysis of traditional versus cloud-based HA architectures, along with future directions involving serverless orchestration and AI-driven failover decision-making. Overall, the review provides a structured roadmap for IT architects seeking to modernize legacy UNIX platforms with the resilience and scalability of AWS.

DOI: https://doi.org/10.5281/zenodo.15803836

Published by: vikaspatanker

Authors: Ms. Samyadevi V Assistant Professor, GuruPrakash VM, Nishbha R, Raagul R

Abstract: A voice-controlled robot is developed to perform accurate and reliable movements in response to spoken commands. The system combines a machine learning-based speech recognition module with an advanced motor control algorithm to enable natural human-robot interaction. The speech module converts voice to text, accurately interpreting commands even in noisy environments and across various accents. Recognized commands are processed and translated into actions, which are executed through a motor control system using pulse-width modulation (PWM) and directional control to manage motor speed and direction. This ensures smooth, synchronized movements, adapting to load changes without delays or jerks. Challenges like background noise and command errors are minimized through noise filtering, adaptive models, and an optimized processing pipeline. The system’s performance—measured by response time, accuracy, and movement reliability—confirms fast and precise execution, showcasing the robot’s effectiveness in real-world scenarios.

DOI:

 

 

Published by: vikaspatanker

Authors: Deepak Raj

Abstract: This review explores the design, implementation, and operational lessons of monitoring KVM-based virtualization on Oracle X8 architectures, as demonstrated by the National Institutes of Health (NIH). In an effort to modernize its research compute infrastructure while maintaining transparency and cost efficiency, NIH deployed an open-source stack consisting of Prometheus, Grafana, libvirt, node exporters, and Oracle ILOM telemetry. The article details how NIH built an end-to-end observability framework that enables real-time monitoring across both physical and virtual layers. The review begins by outlining the importance of monitoring in high-performance and mission-critical environments like NIH, followed by an overview of KVM and Oracle X8 server capabilities. It then delves into the architecture NIH adopted, including hypervisor instrumentation, VM-specific metrics collection, storage I/O profiling, and hardware-level telemetry using Redfish APIs and Oracle ILOM. Emphasis is placed on the practical challenges NIH overcame such as integrating heterogeneous tools, scaling monitoring infrastructure, enforcing security and compliance, and onboarding researchers into self-service observability portals. Security-focused sections discuss hypervisor hardening, auditability under FISMA/NIST mandates, and enforcement of VM isolation. The paper also describes how NIH’s monitoring practices evolved into a modular, GitOps-based approach, enabling repeatable and version-controlled observability deployment. NIH’s roadmap for predictive alerting, hardware-integrated dashboards, and ML-driven anomaly detection rounds out the discussion. By distilling lessons from NIH’s experience, the article offers actionable recommendations for organizations seeking robust virtualization monitoring on commodity hardware. These insights are especially relevant for public sector agencies, research labs, and academic institutions looking to optimize infrastructure transparency and control.

DOI: https://doi.org/10.5281/zenodo.15803863

Published by: vikaspatanker

Authors: Ganapathi Basu

Abstract: The increasing operational complexity of multi-OS Unix environments comprising legacy and modern systems such as Solaris, AIX, HP-UX, Linux, and BSD poses significant challenges for traditional system troubleshooting methodologies. These environments demand high availability, rapid diagnostics, and platform-agnostic observability, which are difficult to achieve using manual scripting and OS-specific tools alone. This review examines how Artificial Intelligence (AI) augments system administration by enabling intelligent diagnostics, predictive monitoring, and automated remediation across heterogeneous Unix infrastructures.Beginning with an overview of Unix's architectural evolution and the interoperability challenges in multi-OS deployments, the article outlines the limitations of conventional troubleshooting practices, including shell-based diagnostics, tribal knowledge, and siloed toolsets. It then explores the application of AI techniques such as machine learning for anomaly detection, natural language processing for log interpretation, and reinforcement learning for adaptive, self-healing responses. AI enables powerful capabilities in log normalization, root cause analysis (RCA), and event correlation especially critical in reducing alert fatigue and accelerating fault isolation. Advanced use cases such as predictive failure detection, behavior modeling, and AI-enhanced capacity planning illustrate the potential of intelligent monitoring. The review further evaluates unified diagnostic platforms like Splunk and Dynatrace, cross-platform frameworks, and real-world AI deployments in multi-OS settings. Key deployment challenges such as data silos, model generalization, and explainability are addressed alongside recommendations for integration with ITSM and DevSecOps pipelines. Emerging trends including AI co-pilots for system administrators, AIOps automation, and observability-as-a-service reflect a future where AI transforms Unix operations from reactive maintenance to autonomous infrastructure resilience. The paper concludes by emphasizing the importance of augmented intelligence where human expertise is amplified, not replaced offering a practical roadmap for AI-driven modernization in Unix ecosystems

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Dhanush Aradhya

Abstract: In enterprise IT environments, efficient server virtualization and domain management are crucial to optimizing hardware utilization, operational agility, and high availability. Oracle Solaris, a flagship Unix operating system renowned for its scalability and security, introduces virtualization constructs such as Logical Domains (LDOMs) and Guest Domains (GDOMs) through its Oracle VM Server for SPARC architecture. These constructs enable fine-grained partitioning of system resources on SPARC hardware, allowing multiple independent OS instances to coexist on a single physical server. However, as the number of domains increases in enterprise deployments, manual provisioning and management become unsustainable. This has led to a growing need for robust automation strategies that can orchestrate domain lifecycle operations with consistency, speed, and minimal administrative overhead.This review article comprehensively examines the architectural principles, automation tools, and orchestration strategies used to manage LDOMs and GDOMs in Oracle Solaris environments. It begins with a detailed explanation of the virtualization framework in Solaris, followed by an exploration of domain architecture and the challenges posed by manual administration. Native tools such as ldm, SMF (Service Management Facility), FMA (Fault Management Architecture), and ZFS are discussed alongside automation methods using Bash and Python scripting. Further, the article evaluates how Oracle tools like Oracle Enterprise Manager and external platforms like Ansible are used to automate provisioning, monitoring, backup, and fault handling for LDOM and GDOM configurations.Real-world case studies illustrate the implementation of these strategies in telecom and financial sectors, highlighting time savings, improved uptime, and reduced human error. The article also discusses the challenges faced during automation, including compatibility issues, security risks, and integration bottlenecks. Looking ahead, it explores the future of AI-driven domain orchestration, RESTful automation interfaces, and hybrid cloud integration. This review provides a strategic and technical foundation for IT architects, system administrators, and automation engineers aiming to optimize their Solaris virtualization environments through effective LDOM and GDOM automation strategies.

DOI: http://doi.org/10.5281/zenodo.15798687

 

 

Published by: vikaspatanker

Authors: Usha Rani

Abstract: – In today’s dynamic enterprise IT landscape, system patching is a critical operation that ensures security, compliance, and performance. Manual patching processes are often fraught with delays, configuration drift, and inconsistencies, leading to potential security breaches and downtime. Automating this process using integrated tools like Red Hat Satellite and Puppet significantly enhances lifecycle management by aligning system states with organizational policies. Red Hat Satellite offers a centralized platform for managing Linux content, lifecycle environments, and host registration, while Puppet provides robust configuration management capabilities for enforcing desired system states. Together, they enable enterprises to deploy, audit, and maintain patches consistently across vast infrastructure landscapes. This review explores the symbiotic relationship between Satellite and Puppet, focusing on how their integration delivers operational efficiency and compliance. It discusses the underlying architecture of each tool, the mechanics of their integration, and the workflow that governs automated patching. The study highlights key functionalities such as content views, CVE mapping, node classification, and patch window orchestration. Additionally, the review presents real-world case studies from financial services, healthcare, and telecom sectors that have adopted this integration for scalable and secure patch management. The article also identifies challenges in implementation, including integration complexity, legacy system compatibility, and potential risks from misclassification or dependency conflicts. Future trends are examined, including the use of AI/ML for predictive patching, ChatOps for collaborative operations, and declarative frameworks for Patch as Code strategies. In conclusion, the integrated use of Satellite and Puppet forms a cornerstone for secure, compliant, and cost-effective system maintenance, empowering IT organizations to proactively manage vulnerabilities while reducing operational overhead.

DOI: https://doi.org/10.5281/zenodo.15798673

 

Published by: vikaspatanker

Authors: Hamid Ansari

Abstract: In today’s digital landscape, enterprise IT environments demand resilient and scalable disaster recovery (DR) solutions, especially in hybrid UNIX systems where Solaris, AIX, HP-UX, and Linux coexist. These systems often run critical workloads in sectors like finance, healthcare, telecommunications, and government, necessitating DR architectures that ensure high availability, data integrity, and business continuity across heterogeneous platforms. This review provides a comprehensive analysis of scalable DR architectures tailored for hybrid UNIX environments, addressing the complex interplay between storage replication, backup strategies, orchestration tools, and operating system-level recovery mechanisms. Key architectural patterns such as active-active and multi-site replication models are examined alongside file system-level and block-level replication technologies including ZFS send/receive, Veritas Volume Replicator, and SAN mirroring solutions. The paper compares OS-specific recovery tools like Ignite-UX, mksysb, and Solaris Unified Archives, and assesses their interoperability in multi-vendor environments. Further, the study explores the orchestration layer of disaster recovery, highlighting the role of configuration management and automation tools like Ansible, Puppet, and scripting frameworks. Monitoring, testing, and policy-driven recovery are addressed as essential pillars of a sustainable DR strategy. Real-world case studies are analyzed to illustrate practical implementations, performance outcomes, and lessons learned in deploying scalable DR across diverse UNIX infrastructures. Challenges such as format incompatibility, network reconfiguration, and security hardening are critically discussed. Finally, the review anticipates emerging trends, including the use of AI/ML for proactive fault prediction and the integration of DR into continuous compliance and observability pipelines. This article serves as a reference for system architects, disaster recovery planners, and enterprise IT professionals seeking to build resilient, automated, and cross-platform DR frameworks for UNIX-centric infrastructures.

DOI: http://doi.org/10.5281/zenodo.15798539

Published by: vikaspatanker

Authors: Albert Joshep

Abstract: Predictive maintenance is an emerging discipline that combines system telemetry, machine learning, and automation to preemptively identify and resolve failures in complex computing environments. This review explores the implementation of predictive maintenance in Solaris and Red Hat Enterprise Linux (RHEL) platforms two prominent Unix-based systems widely deployed across enterprise IT landscapes. By comparing architectural features, telemetry sources, and modeling techniques, the study highlights both the unique capabilities and challenges presented by each operating system. Solaris benefits from a robust fault management architecture (FMA), advanced diagnostics like DTrace, and SPARC hardware optimization, making it well-suited for hardware-level monitoring. Red Hat, on the other hand, excels in automation, scalability, and hybrid cloud compatibility through tools such as Red Hat Insights, Ansible, and Performance Co-Pilot. The article delves into key predictive modeling strategies including time-series forecasting, anomaly detection, and classification, utilizing methods ranging from ARIMA and Isolation Forests to neural networks. Integration and automation workflows are examined, showcasing how Unix-native tools and open-source frameworks are used to train, deploy, and act upon model predictions. Through case studies, the review quantifies the benefits of predictive maintenance, including reduced mean time to recovery (MTTR), enhanced SLA adherence, and cost savings. Finally, it discusses limitations such as data inconsistency, model drift, and cross-platform transferability, while outlining future directions including AI co-pilots, self-learning systems, and Predictive Maintenance-as-a-Service (PMaaS). By offering a detailed comparative analysis and strategic recommendations, this review serves as a practical guide for enterprises aiming to implement or enhance predictive maintenance in mixed Unix environments.

DOI: https://doi.org/10.5281/zenodo.15798515

Published by: vikaspatanker