Enhancing Library Resource Sharing with Cloud- Based Library Management System and Virtual Private Network Integration: A Conceptual Framework

S. Balachandran; J. Dominic; S. Sivankalai

doi:10.17821/srels/2025/v62i3/171579

Authors

S. Balachandran
Department of Library and Information Science, Hindustan Institute of Technology and Science (A Deemed to be University), Chennai – 603103, Tamil Nadu
J. Dominic
Department of Library and Information Science, Hindustan Institute of Technology and Science (A Deemed to be University), Chennai – 603103, Tamil Nadu
S. Sivankalai
Central Library, Anna University, Chennai – 600025, Tamil Nadu

DOI:

https://doi.org/10.17821/srels/2025/v62i3/171579

Keywords:

Artificial Intelligence, Cloud Computing, Library Management System, Library Repository, Natural Language Processing, Virtual Private Network

Abstract

A significant challenge in developing an efficient method for sharing resources across institutions is utilising cloud computing, networking, virtualisation, and web technologies. Integrating college libraries under a single platform with a common Library Management System (LMS) can greatly enhance resource sharing. This study introduces a conceptual framework for a Cloud and Virtual Private Network (VPN) based universal LMS, designed to facilitate the sharing of library resources among multiple institutions. The core contribution of this research is a cloud-based LMS that unifies college libraries on one platform and enables remote access to e-resources, promoting efficient resource distribution. To assess the challenges and needs of library professionals in adopting this approach, a survey was conducted with 51 librarians from Tamil Nadu using a snowball sampling technique. The survey aimed to uncover difficulties with existing library software and to gauge the perception of cloud and VPN technologies for improving library services. Analysis using a threepoint Likert-like scale revealed valuable insights into the benefits, challenges, and readiness of professionals to embrace these solutions. The findings guided the design of our system architecture, ensuring it meets librarians’ practical needs while offering scalability, security, and efficiency. By integrating Artificial Intelligence (AI) driven face recognition, Machine Learning (ML), and Natural Language Processing (NLP), the proposed system provides a unified, user-centric approach to library automation, aligning with modern technological advancements to meet diverse user requirements.

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