Daily Archives: July 19, 2025

Authors: Rezwana Akter, Mehedi Munna

Abstract: In today's interconnected and highly digital world, businesses of all sizes are increasingly reliant on their IT infrastructure for the delivery of services, and the availability of critical systems is more important than ever. For enterprises running on Red Hat Enterprise Linux (RHEL) environments, maintaining business continuity in the event of an unforeseen disaster requires intelligent disaster recovery (DR) strategies. These workflows are essential for ensuring that the systems remain resilient against failures such as hardware malfunctions, cyberattacks, or natural disasters. Intelligent disaster recovery workflows in RHEL environments are designed to ensure that critical business functions continue seamlessly by automating backup, replication, and failover processes, ensuring minimal downtime and maximum recovery efficiency. This paper explores intelligent disaster recovery workflows tailored for Red Hat Enterprise environments, focusing on leveraging automation tools like Ansible, Red Hat Virtualization (RHV), and Red Hat OpenShift to orchestrate these processes effectively. Through automation, organizations can not only streamline their recovery procedures but also enhance scalability and reduce recovery times, ensuring the availability of mission-critical data and services in the event of system failures. Moreover, as more businesses adopt hybrid cloud infrastructures, integrating cloud-based disaster recovery (DR) solutions with traditional on-premise solutions becomes vital. This paper also discusses how businesses can implement cloud disaster recovery strategies alongside on-premise infrastructures to create a comprehensive and scalable disaster recovery plan that minimizes risks associated with data loss while ensuring compliance with regulations such as HIPAA and GDPR.

DOI: https://doi.org/10.5281/zenodo.16152661

Published by: vikaspatanker

Authors: Nasrin Jahan, Salman Hossain

Abstract: In healthcare organizations, the protection of patient data and operational continuity is paramount. The advent of digital health systems has led to a vast increase in data generation, from Electronic Health Records (EHRs) to medical imaging, creating challenges for data protection. Healthcare IT systems must meet stringent regulatory compliance standards like HIPAA (Health Insurance Portability and Accountability Act), which dictate that data be secure, available, and recoverable in case of failure. With data volumes increasing, ensuring fast and efficient backup processes has become critical, as it directly impacts an organization’s ability to respond to unforeseen disruptions, disasters, or cyberattacks. One of the leading solutions for optimizing backup operations in healthcare IT is the VSP G900/G1000 arrays from Hitachi Vantara. These storage systems are designed to provide high performance, scalability, and reliability in complex environments, offering a combination of features such as data deduplication, replication, cloud integration, and high availability. These features enable healthcare organizations to optimize their backup workflows, reduce storage costs, and ensure rapid data recovery, making them a valuable asset in modern healthcare infrastructures. This paper explores how VSP G900/G1000 arrays optimize backup strategies within healthcare IT environments. It examines how these arrays facilitate data protection, meet regulatory compliance standards, and enhance disaster recovery capabilities. Additionally, the paper discusses the scalability and flexibility of these arrays in handling large healthcare data sets, enabling efficient backup and recovery while ensuring that the IT systems stay operational with minimal downtime. As healthcare organizations increasingly adopt cloud-first strategies, the VSP G900/G1000 arrays are positioned as a vital tool for protecting sensitive data, ensuring business continuity, and optimizing backup processes across the healthcare sector.

DOI: https://doi.org/10.5281/zenodo.16152432

Published by: vikaspatanker

Authors: Ishara Jayasuriya, Chamika Dissanayake

Abstract: In today’s ever-evolving technological landscape, enterprises are increasingly relying on cloud computing for its scalability, flexibility, and cost-effectiveness. As organizations grow and expand, managing large-scale data environments efficiently becomes a significant challenge. One of the most crucial aspects of managing such environments is data protection, which is why snapshot technologies have become essential components of modern IT infrastructures. Snapshots enable organizations to create point-in-time copies of their systems and data, allowing for quick recovery in case of failure, and are particularly useful for backup, disaster recovery, and ensuring data integrity. Among the most widely used snapshot technologies are IBM Tivoli Storage Manager (TSM) and Commvault, both of which offer advanced backup and snapshot management solutions suited for large-scale environments. IBM Tivoli Storage Manager (TSM) has been an industry leader in backup and recovery for enterprises, with a strong emphasis on data deduplication and incremental backup technologies. On the other hand, Commvault is renowned for its cloud-first approach to data protection, providing comprehensive backup, recovery, and snapshot solutions across on-premises, hybrid, and cloud environments. This paper aims to compare the snapshot capabilities of TSM and Commvault, focusing on their performance, scalability, integration with cloud environments, data integrity features, compliance support, and overall suitability for large-scale IT environments. The comparison will explore the differences in how these two snapshot technologies handle large volumes of data and complex infrastructure setups, such as multi-cloud and hybrid cloud architectures. By examining the strengths and weaknesses of both solutions, this paper will provide organizations with valuable insights to make informed decisions about which snapshot technology best fits their operational needs. For businesses dealing with high-volume workloads, the ability to perform fast, reliable backups and recovery is critical. This analysis will also explore how both TSM and Commvault contribute to efficient disaster recovery, support regulatory compliance, and ensure business continuity in large-scale environments. Ultimately, this comparison will assist enterprises in selecting the most appropriate snapshot technology for their infrastructure, ensuring data protection while optimizing cost, performance, and scalability.

Published by: vikaspatanker

Authors: Bhanuka Silva, Sanduni Jayalath

Abstract: In the digital age, data integrity and availability are the cornerstones of business continuity. Organizations rely on backup systems to ensure that their data is always protected and recoverable in the event of a failure. However, backup failures pose a significant risk, especially when they go unnoticed for extended periods, leading to data loss or extended downtime. Traditional methods of managing backup failures often involve manual intervention, which is both time-consuming and prone to human error. The advent of automation provides a solution to these challenges by enabling the auto-remediation of backup failures. This paper explores the concept of auto-remediation of backup failures using shell-based schedulers, particularly cron in Unix-like systems, to detect, address, and resolve backup issues automatically. Auto-remediation refers to the use of automation to detect backup failures and trigger predefined remediation actions, such as restarting failed jobs, adjusting configurations, or alerting system administrators. By combining cron jobs and shell scripts, businesses can automate the backup monitoring process, reducing the time and effort required to manage backup systems manually. The integration of this automation into a larger backup management framework ensures that failures are promptly addressed, minimizing the risks associated with data loss and downtime. This paper highlights how cron-based automation can create efficient and scalable backup systems that are resilient, proactive, and reliable, ultimately supporting business continuity and improving data protection efforts.

DOI: https://doi.org/10.5281/zenodo.16151937

Published by: vikaspatanker

Authors: Dr. Kumarswamy S, Darshith L, Aditya B N, Mohammed Waseem, Nagraj, Nitin Reddy

Abstract: This review explores the effectiveness of Artificial Intelligence (AI) methods in software testing, addressing the high costs and challenges of traditional approaches. It examines key AI and Machine Learning (ML) techniques, their applications in test suite optimization, test case generation, and test case prioritization, highlighting quantitative improvements. The paper also discusses current challenges like data availability and model bias, and outlines future research directions for more adaptable and scalable AI solutions in software engineering.

DOI: https://doi.org/10.5281/zenodo.16149448

Published by: vikaspatanker

Authors: Anirudh Narayan, Bindu Lakshmi, Haritha Gopal, Vivek Vardhan

Abstract: In the evolving landscape of IT infrastructure monitoring, the volume and velocity of log data generated by tools such as Nagios and Zabbix present significant challenges for timely and accurate anomaly detection. Traditional rule-based approaches, which rely on static thresholds and manual configurations, often fail to capture subtle or emerging issues, leading to alert fatigue or missed incidents. To address these limitations, the integration of artificial intelligence, particularly machine learning, into log-based monitoring has emerged as a transformative solution. By analyzing patterns in historical logs and adapting dynamically to changes in system behavior, AI models ranging from supervised classifiers to unsupervised clustering algorithms and deep learning architectures can enhance the detection of anomalies within Nagios and Zabbix environments. This review examines the application of AI to anomaly detection in logs generated by Nagios and Zabbix, focusing on key log types such as performance metrics, event logs, alert logs, and syslogs. It explores how AI improves detection precision, reduces false positives, and enables earlier incident prediction. The paper also compares data handling mechanisms in both tools and outlines common AI integration pipelines including log preprocessing, model training, and real-time inference. Furthermore, implementation case studies and evaluation metrics are discussed to highlight real-world benefits and performance trade-offs. Ultimately, this article positions AI-driven anomaly detection as a critical enabler for modern observability and proactive IT operations, especially in large-scale or mission-critical infrastructures.

Published by: vikaspatanker

Authors: Mayank Shakkerwal

Abstract: The Deep Neural Networks (DNNS) has revolutionized the field of artificial intelligence by enabling machines to learn from large amounts of data with human-level accuracy in tasks such as image recognition, Natural Language Processing (NLP), and game playing. This Research Paper represents a comprehensive observation of the structure and function of DNN, great advances in their development, popular architecture, real -world applications and major challenges that hinder their widespread adoption. We also highlight future instructions in DNN research, including interpretation, efficiency and ethical/moral implications.

Published by: vikaspatanker