Village Data Backup and Disaster Recovery: A Comparative Study of Cloud Solutions with Traditional Methods

This study presents a comparative analysis of traditional disk-based and cloud-based Backup and Disaster Recovery (DR) approaches, focusing on the challenges inherent in existing solutions, such as high infrastructure costs, extended recovery times, and operational disruptions during software updates. Conventional disk-based systems often require periodic reboots and manual interventions, which can interrupt ongoing operations. In contrast, cloud-based solutions, particularly Asigra, offer a streamlined alternative by reducing infrastructure dependency, enhancing recovery metrics—specifically Recovery Point Objective (RPO) and Recovery Time Objective (RTO)—and minimizing maintenance downtime through agentless and incremental backups. Cloud backup provides comprehensive upgrades without the need for system reboots, thereby saving time and improving operational effectiveness. This study evaluates the proposed cloud-based approach in a village government organizational environment, analyzing its performance based on Total Cost of Ownership (TCO), RPO, and RTO. Cloud-based configurations are compared with traditional setups to assess improvements in disaster recovery procedures and data storage. Findings demonstrate that cloud-based strategies offer simpler and more efficient DR solutions, providing superior scalability, reliability, and administrative ease tailored to the unique needs of village government data management.

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Publication Details

Journal
Journal of Information Technology and Computer Science
Published
2024-12-23
DOI
https://doi.org/10.25126/jitecs.93681
Primary Topic
Internet of Things and AI
Type
article
Field-Weighted Citation Impact
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article

Village Data Backup and Disaster Recovery: A Comparative Study of Cloud Solutions with Traditional Methods

Barlian Henryranu Prasetio, Adi Setiawan, Edita Rosana Widasari, Hanifa Maulani Ramadhan
Journal of Information Technology and Computer Science
Internet of Things and AI
article

Village Data Backup and Disaster Recovery: A Comparative Study of Cloud Solutions with Traditional Methods

Barlian Henryranu Prasetio, Adi Setiawan, Edita Rosana Widasari, Hanifa Maulani Ramadhan
article en

Abstract

This study presents a comparative analysis of traditional disk-based and cloud-based Backup and Disaster Recovery (DR) approaches, focusing on the challenges inherent in existing solutions, such as high infrastructure costs, extended recovery times, and operational disruptions during software updates. Conventional disk-based systems often require periodic reboots and manual interventions, which can interrupt ongoing operations. In contrast, cloud-based solutions, particularly Asigra, offer a streamlined alternative by reducing infrastructure dependency, enhancing recovery metrics—specifically Recovery Point Objective (RPO) and Recovery Time Objective (RTO)—and minimizing maintenance downtime through agentless and incremental backups. Cloud backup provides comprehensive upgrades without the need for system reboots, thereby saving time and improving operational effectiveness. This study evaluates the proposed cloud-based approach in a village government organizational environment, analyzing its performance based on Total Cost of Ownership (TCO), RPO, and RTO. Cloud-based configurations are compared with traditional setups to assess improvements in disaster recovery procedures and data storage. Findings demonstrate that cloud-based strategies offer simpler and more efficient DR solutions, providing superior scalability, reliability, and administrative ease tailored to the unique needs of village government data management.

Journal of Information Technology and Computer ScienceVol. 9(3)
University of Brawijaya (ID)
Openalex Percentile: Top 53%
Internet of Things and AI
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Village Data Backup and Disaster Recovery: A Comparative Study of Cloud Solutions with Traditional Methods — Barlian Henryranu Prasetio, Adi Setiawan, et al. · Journal of Information Technology and Computer Science (2024) | TGRS Research Map | TGRS