https://www.adrjournalshouse.com/index.php/cloud-computing-web-applications/issue/feed 2026-05-05T19:01:47+00:00 Advanced Research Publications info@adrpublications.in Open Journal Systems Journal of Advanced Research in Cloud Computing, Virtualization and Web Applications https://www.adrjournalshouse.com/index.php/cloud-computing-web-applications/article/view/2656 2026-05-05T19:01:47+00:00 Kamlesh Maharwal librarian.block-c@jecrc.ac.in Rekha Mithal librarian.block-c@jecrc.ac.in Vijeta Kumawat librarian.block-c@jecrc.ac.in Saguna Chaturvedi librarian.block-c@jecrc.ac.in

<p><strong>The services offered by the college’s central library of Jaipur were assessed. Data was collected through a questionnaire given to library users, and an evaluation of the services was carried out. The outcomes of the study designate that the central library in colleges contributes to the provision. Variety of library services, including the RFID (Radio Frequency Identification) software, for the users. The recent state of libraries gives opportunities for self-learning to the beneficiaries. Respondents recommended that the library provide training programmes to assist users in making better use of its resources.</strong></p> <p><strong>DOI:</strong> https://doi.org/10.24321/3051.4320.202609</p>

2026-05-15T00:00:00+00:00 Copyright (c) 2026 Journal of Advanced Research in Cloud Computing, Virtualization and Web Applications https://www.adrjournalshouse.com/index.php/cloud-computing-web-applications/article/view/2544 2026-03-10T12:15:20+00:00 Ritu Singh ritusingh.cse@huroorkee.ac.in Praveen Kumar ritusingh.cse@huroorkee.ac.in Sandeep Kumar ritusingh.cse@huroorkee.ac.in

<p>The high rate of growth of cloud computing services has made effective management of resources inseparable from the maintenance of service performance and, at the same time, reducing the costs of operations. Traditional methods of autoscaling tend to use reactive rules that are based on thresholds, which are not sufficient to respond to the dynamic and unpredictable workload patterns that are seen in the modern cloud settings. To overcome this shortcoming, we introduce a new framework that uses text analytics with natural language processing capabilities to derive workload indicators based on heterogeneous cloud telemetry data, such as logs, metrics, user feedback, and service tickets, to support the intelligent workload prediction and proactive autoscaling. In the suggested methodology, the textual cloud data is transformed to semantically meaningful features, which are then integrated into hybrid forecasting models, which initiate proactive scaling decisions. Experimental analysis of actual cloud traces and model workload conditions shows significant gains in prediction error, resource utilisation and response latency compared to baseline methods.</p>

2026-03-10T00:00:00+00:00 Copyright (c) 2026 Journal of Advanced Research in Cloud Computing, Virtualization and Web Applications