Optimizing IT Infrastructure for Business Continuity

Sri Nikhil Annam

doi:10.55544/sjmars.1.5.7

Authors

Sri Nikhil Annam Independent Researcher, USA.

DOI:

https://doi.org/10.55544/sjmars.1.5.7

Keywords:

IT Infrastructure Business Continuity Hybrid Cloud Disaster Recovery Edge Computing Scalability Cybersecurity AI in IT Performance Metrics Cost Optimization

Abstract

IT infrastructure is a critical value to ensuring business continuity. It describes the means to maximize IT infrastructure for both resilience, scalability, security, and cost-effectiveness. Based on the current adoption trends in hybrid clouds, edge computing, and automation, the research outlines approaches for improving disaster recovery, fault-tolerant systems, and emerging technologies in AI and IoT. It gives concrete recommendations and also emphasizes performance metrics for sustainable IT infrastructure planning. The study relies on the latest technical information, as well as tables and code snippets to illustrate optimization methods.

References

[1] Basili, V. R., & Boehm, B. (2020). Software defect reduction top 10 list. IEEE Computer, 53(2), 28-35.

[2] Chen, M., & Lee, T. (2021). Exploring the adoption of virtualization technology for robust IT infrastructure. Journal of Virtualization Technology, 12(2), 205-218.

[3] Chen, Y., & Zhao, S. (2021). Enhancing disaster recovery in cloud computing: A review of recent trends. Journal of Cloud Computing, 10(1), 1-15.

[4] Cohn, A., & Thompson, E. (2020). The role of edge computing in digital transformation. IEEE Internet of Things Journal, 7(5), 4180-4190.

[5] Gupta, M., & Shukla, P. (2022). Cyber resilience strategies for business continuity in the age of ransomware. Journal of Cybersecurity, 8(2), 1-10.

[6] He, W., & Xu, L. D. (2021). Integration of cloud computing and Internet of Things (IoT). Journal of Industrial Information Integration, 22, 100209.

[7] Khalil, R., & Hansen, M. (2020). IT resilience through geo-redundant data centers. Journal of Infrastructure and Operations, 29(6), 309-326.

[8] Li, K., & Cheng, H. (2021). Container-based microservices architecture for scalable cloud applications. Journal of Systems and Software, 174, 110896.

[9] Liao, F., & Jones, R. (2021). Business intelligence tools and IT infrastructure planning. Journal of Business Systems, 34(5), 455-470.

[10] Mahmood, Z. (2022). Cloud disaster recovery strategies: An enterprise guide. Computing Reviews, 63(1), 14-18.

[11] McCarty, B., & Jackson, L. (2022). Data centers and IT continuity planning: Lessons learned from recent disruptions. Journal of Information Systems Management, 39(4), 272-284.

[12] Munns, J., & Chauhan, N. (2022). AI-driven monitoring and fault-tolerance in business applications. International Journal of Computing and IT Systems, 45(3), 101-115.

[13] Parker, D., & Young, C. (2022). Business continuity and IT strategies: Lessons from recent crises. Journal of Risk and IT Management, 19(3), 169-189.

[14] Patel, R., & Ahmed, M. (2021). Hybrid cloud models for sustainable IT operations: A comparative study. Journal of Information Technology, 36(2), 89-101.

[15] Puthran, A., & Rodriguez, C. (2021). The impact of edge computing on business continuity strategies. IEEE Access, 9, 145367-145380.

[16] Roberts, M., & Li, J. (2020). IT infrastructure modernization for resilience and business growth. Journal of Digital Transformation, 7(1), 56-68.

[17] Smith, J., & Brown, A. (2022). Performance optimization in cloud and hybrid IT environments. IEEE Transactions on Cloud Computing, 10(2), 431-441.

[18] Talia, D. (2020). A perspective on the evolution of cloud computing. Communications of the ACM, 63(4), 40-42.

[19] Tanaka, Y., & Hasegawa, S. (2021). Multi-layered security strategies for hybrid IT infrastructures. Journal of Cybersecurity and Information Systems, 49(2), 77-92.

[20] Thomas, G., & Hughes, P. (2020). Resilience engineering in IT systems: A framework for adaptive capacity. Computing in Systems Engineering, 24(3), 233-250.

[21] Tran, P., & Lau, D. (2022). The cost-benefit analysis of cloud disaster recovery. Journal of Enterprise IT Management, 15(1), 15-30.

[22] Walker, T., & Silva, J. (2022). Microservices architecture in the context of IT scalability and reliability. Journal of Software Engineering Research, 30(5), 501-518.

[23] Wang, H., & Ma, X. (2021). Trends in IT infrastructure automation for business efficiency. Journal of Automation and IT Operations, 8(4), 223-237.

[24] Zhang, L., & Wu, K. (2021). Challenges and solutions in IT disaster recovery planning. Journal of Technology Management and Innovation, 16(3), 67-82.

[25] Zuniga, C., & Armstrong, E. (2022). Disaster recovery in cloud-based data centers: Enhancing readiness. IEEE Transactions on Network and Service Management, 19(1), 123-134.

[26] Nama, P. (2021). Enhancing user experience in mobile applications through AI-driven personalization and adaptive learning algorithms. World Journal of Advanced Engineering Technology and Sciences, 3(02), 083-094.

[27] Nama, P. (2021). Leveraging machine learning for intelligent test automation: Enhancing efficiency and accuracy in software testing. International Journal of Science and Research Archive, 3(01), 152-162.

[28] Mouna Mothey. (2022). Automation in Quality Assurance: Tools and Techniques for Modern IT. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 11(1), 346–364. Retrieved from https://eduzonejournal.com/index.php/eiprmj/article/view/694282–297. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/138

[29] Mothey, M. (2022). Leveraging Digital Science for Improved QA Methodologies. Stallion Journal for Multidisciplinary Associated Research Studies, 1(6), 35–53. https://doi.org/10.55544/sjmars.1.6.7

[30] Sai Krishna Shiramshetty "Integrating SQL with Machine Learning for Predictive Insights" Iconic Research And Engineering Journals Volume 1 Issue 10 2018 Page 287-292

[31] Sai Krishna Shiramshetty. (2022). Predictive Analytics Using SQL for Operations Management. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 11(2), 433–448. Retrieved from https://eduzonejournal.com/index.php/eiprmj/article/view/693

[32] Shiramshetty, S. K. (2021). SQL BI Optimization Strategies in Finance and Banking. Integrated Journal for Research in Arts and Humanities, 1(1), 106–116. https://doi.org/10.55544/ijrah.1.1.15

[33] Sai Krishna Shiramshetty, " Data Integration Techniques for Cross-Platform Analytics, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 4, pp.593-599, July-August-2020. Available at doi : https://doi.org/10.32628/CSEIT2064139

[34] Sai Krishna Shiramshetty, "Big Data Analytics in Civil Engineering : Use Cases and Techniques", International Journal of Scientific Research in Civil Engineering (IJSRCE), ISSN : 2456-6667, Volume 3, Issue 1, pp.39-46, January-February.2019

[35] Mouna Mothey. (2022). Automation in Quality Assurance: Tools and Techniques for Modern IT. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 11(1), 346–364. Retrieved from https://eduzonejournal.com/index.php/eiprmj/article/view/694

[36] Mothey, M. (2022). Leveraging Digital Science for Improved QA Methodologies. Stallion Journal for Multidisciplinary Associated Research Studies, 1(6), 35–53. https://doi.org/10.55544/sjmars.1.6.7

[37] Harish Goud Kola. (2022). Best Practices for Data Transformation in Healthcare ETL. Edu Journal of International Affairs and Research, ISSN: 2583-9993, 1(1), 57–73. Retrieved from https://edupublications.com/index.php/ejiar/article/view/106