From Outages to Excellence: Building Resilience with Disaster Recovery in the Cloud
DOI:
https://doi.org/10.32628/CSEIT251112208Keywords:
Cloud Disaster Recovery, Business Continuity, Automated Failover, Multi-region Resilience, Recovery AutomationAbstract
Cloud-based disaster recovery (DR) has emerged as a transformative approach to building organizational resilience, fundamentally changing how businesses protect their critical systems and data. This article explores the evolution from traditional DR methods to modern cloud-based solutions, examining their architectural components, implementation strategies across various industries, and integration with advanced technologies. Through analysis of real-world applications and case studies, it demonstrates how cloud DR solutions enable organizations to achieve superior recovery objectives while maintaining cost-effectiveness and scalability. This article highlights the significance of automated monitoring, AI-driven anomaly detection, and continuous testing in establishing robust DR strategies. Furthermore, it focuses on industry-specific challenges and solutions, providing insights into best practices for optimizing cloud DR implementations. It also indicates that organizations embracing cloud-based DR not only enhance their business continuity capabilities but also gain strategic advantages in adaptability and operational excellence. This comprehensive article on cloud DR encompasses current practices, emerging trends, and future challenges, offering valuable insights for technology leaders and practitioners in the field of disaster recovery and business continuity.
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References
InterVision, "The Ultimate Guide to Disaster Recovery as a Service (DRaaS)," InterVision Technical Publication, Oct. 2024. [Online]. Available: https://intervision.com/wp-content/uploads/2024/10/IV-Ultimate-Guide-to-DRaaS.pdf.
Aggidi Sathya and Bhuvana J, "Cloud Disaster Recovery Management and Business Continuity," International Research Journal of Modernization in Engineering Technology and Science, vol. 6, no. 5, May 2024. [Online]. Available: https://www.irjmets.com/uploadedfiles/paper//issue_5_may_2024/57003/final/fin_irjmets1716312021.pdf
Venkata Jagadeesh Reddy Kopparthi, "Architecture and Implementation of Cloud-Based Disaster Recovery," International Journal For Multidisciplinary Research, Dec. 2024. [Online]. Available: https://www.researchgate.net/publication/387938245_Architecture_and_Implementation_of_Cloud-Based_Disaster_Recovery
Ji-Beom Kim et al., "Design and Implementation of an Automated Disaster-recovery System for a Kubernetes Cluster Using LSTM," arXiv Computer Science, 2024. [Online]. Available: https://arxiv.org/pdf/2402.02938
Suhas Lakum, "Advanced Disaster Recovery Strategies for Hybrid Cloud Environments: A Comprehensive Technical Guide," International Journal of Computer Engineering and Technology, vol. 15, no. 6, Dec. 2024. [Online]. Available: https://iaeme.com/MasterAdmin/Journal_uploads/IJCET/VOLUME_15_ISSUE_6/IJCET_15_06_095.pdf[6] ClearData Healthcare Cloud, "Healthcare Cloud Backup & Disaster Recovery," ClearData Technical Publication, Jun. 2023. [Online]. Available: https://www.cleardata.com/wp-content/uploads/2023/06/SET-MKTG-WP-33-Backup-Disaster-Recovery.pdf
Elijah William, "Enhancing Disaster Recovery in the Cloud with AI Capabilities," ResearchGate Technical Publication, Dec. 2024. [Online]. Available: https://www.researchgate.net/publication/387125805_Enhancing_Disaster_Recovery_in_the_Cloud_with_AI_Capabilities
Hassan Continuity et al, "Cloud Disaster Recovery: Planning and Implementing Business," ResearchGate Technical Publication, Aug. 2023. [Online]. Available: https://www.researchgate.net/publication/372826112_Cloud_Disaster_Recovery_Planning_and_Implementing_Business Seethala, S. C. (2024). AI-Driven Data Warehousing for Financial Institutions: Future-Proofing Against Market Volatility. Journal of Scientific and Engineering Research, 11(5), 309–314. https://doi.org/10.5281/zenodo.14059593
Enrico Barbierato et al., "Cost- and performance-based evaluation of cloud-based disaster recovery," ECMS International Conference on Modelling and Simulation, 2023. [Online]. Available: https://www.scs-europe.net/dlib/2023/ecms2023acceptedpapers/0568_dis_ecms2023_0082.pdf. Seethala, S. C. (2024). Next-Level Data Warehousing in Healthcare: AI-Powered Automation for Real-Time Patient Data. Journal of Artificial Intelligence, Machine Learning, and Data Science, 2(3), 351. https://doi.org/10.51219/JAIMLD/srinivasa-chakravarthy/351
Ken Drinkwater et al., "Integrating Cybersecurity and Disaster Recovery: A Unified Approach to Business Continuity," ResearchGate Technical Publication, April 2022. [Online]. Available: https://www.researchgate.net/publication/383268245_Integrating_Cybersecurity_and_Disaster_Recovery_A_Unified_Approach_to_Business_Continuity
Robert Kellerman, "The Future of Disaster Recovery: Embracing Cloud, AI, and Outsmarting New Threats," Stage2Data Technical Publication. [Online]. Available: https://stage2data.com/future-of-disaster-recovery-draas-cloud-ai/
Prithvish Kovelamudi, "Disaster Recovery in the Cloud: Ensuring Business Continuity Across Distributed Systems," IEEE Computer Society, 18 Oct. 2024. [Online]. Available: https://www.computer.org/publications/tech-news/trends/disaster-recovery-in-the-cloud.
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