Technology & Management Review

Peer Reviewed Article

Vol. 3 (2018)

Building Secure and Scalable Applications on Azure Cloud: Design Principles and Architectures

Submitted
2024 July 28
Published
2018-04-25

Abstract

This article explores the integration of advanced security practices, scalable architectural patterns, and compliance and performance monitoring in Azure Cloud environments to address the critical research gap in developing robust cloud applications. The primary objective of this study is to provide a comprehensive framework for enhancing security, scalability, and compliance in Azure deployments. Through an in-depth analysis of Azure's tools and services, the study highlights the benefits of microservices architecture, serverless computing, containerization, and proactive monitoring. Principal findings reveal that a holistic approach, combining these elements, ensures continuous compliance, optimal performance, and dynamic scalability. The policy implications suggest that organizations should adopt integrated strategies, leveraging Azure's capabilities to meet regulatory standards, enhance security, and optimize resource utilization. These insights offer valuable guidelines for organizations aiming to improve their cloud application development and management processes, ultimately delivering high-quality, reliable services in a dynamic digital landscape.

References

  1. Bharadi, V. A., & Meena, M. (2015). Novel architecture for CBIR SAAS on Azure cloud. The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, 366-371. https://doi.org/10.1109/INFOP.2015.7489409
  2. Bhardwaj, A., Singh, V. K., Vanraj, V., & Narayan, Y. (2015). Analyzing BigData with Hadoop cluster in HDInsight azure Cloud. The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, 1-5. https://doi.org/10.1109/INDICON.2015.7443472
  3. Chen, P., Lee, E., & Wang, L. (2013). A cloud-based synthetic seismogram generator implemented using Windows Azure. Earthquake Science, 26(5), 321-329. https://doi.org/10.1007/s11589-013-0038-8
  4. Costan, A., Tudoran, R., Antoniu, G., & Brasche, G. (2016). TomusBlobs: scalable data-intensive processing on Azure clouds. Concurrency and Computation: Practice & Experience, 28(4), 950-976. https://doi.org/10.1002/cpe.3034
  5. Hoske, M. T. (2014). Microsoft Azure cloud platform connects with Rockwell Automation as first industrial partner. Control Engineering, 61(7).
  6. Kim, I., Jung, J., DeLuca, T. F., Nelson, T. H., & Wall, D. P. (2012). Cloud Computing for Comparative Genomics with Windows Azure Platform. Evolutionary Bioinformatics, 8, 527.
  7. Lu, S., Ranjan, R., & Strazdins, P. (2015). Reporting an experience on design and implementation of e-Health systems on Azure cloud. Concurrency and Computation: Practice & Experience, 27(10), 2602-2615. https://doi.org/10.1002/cpe.3325
  8. Mohammed, M. A., Kothapalli, K. R. V., Mohammed, R., Pasam, P., Sachani, D. K., & Richardson, N. (2017). Machine Learning-Based Real-Time Fraud Detection in Financial Transactions. Asian Accounting and Auditing Advancement, 8(1), 67–76. https://4ajournal.com/article/view/93
  9. Mohammed, R., Addimulam, S., Mohammed, M. A., Karanam, R. K., Maddula, S. S., Pasam, P., & Natakam, V. M. (2017). Optimizing Web Performance: Front End Development Strategies for the Aviation Sector. International Journal of Reciprocal Symmetry and Theoretical Physics, 4, 38-45. https://upright.pub/index.php/ijrstp/article/view/142
  10. Mrozek, D., Gosk, P., & Małysiak-Mrozek, B. (2015). Scaling Ab Initio Predictions of 3D Protein Structures in Microsoft Azure Cloud. Journal of Grid Computing, 13(4), 561-585. https://doi.org/10.1007/s10723-015-9353-8
  11. Persico, V., Marchetta, P., Botta, A., & Pescape, A. (2014). On Network Throughput Variability in Microsoft Azure Cloud. The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings., 1-6. https://doi.org/10.1109/GLOCOM.2014.7416997
  12. Sachani, D. K., & Vennapusa, S. C. R. (2017). Destination Marketing Strategies: Promoting Southeast Asia as a Premier Tourism Hub. ABC Journal of Advanced Research, 6(2), 127-138. https://doi.org/10.18034/abcjar.v6i2.746
  13. Shanahan, H. P., Owen, A. M., & Harrison, A. P. (2014). Bioinformatics on the Cloud Computing Platform Azure. PLoS One, 9(7). https://doi.org/10.1371/journal.pone.0102642
  14. Ying, D., Patel, B., & Dhameliya, N. (2017). Managing Digital Transformation: The Role of Artificial Intelligence and Reciprocal Symmetry in Business. ABC Research Alert, 5(3), 67–77. https://doi.org/10.18034/ra.v5i3.659

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