An Empirical Study of Secure Cloud Database Refactoring on Application Scalability and Fault Tolerance

Article Sidebar

PDF
Published: 2019-12-03
DOI: https://doi.org/10.15662/IJRPETM.2019.0206003
Keywords:
Secure cloud refactoring, Database sharding, Data tokenization, Attribute-based access control (ABAC), Horizontal scalability, Fault tolerance, Recovery Time Objective (RTO)

Main Article Content

Sushmitha Yarlagadda
Independent Researcher, New Jersey, USA

Abstract

The continuous evolution of cloud-native applications necessitates periodic database refactoring to address performance bottlenecks, accommodate new features, and, crucially, integrate enhanced security mechanisms. This paper presents an Empirical Study of Secure Cloud Database Refactoring (SCDR), focusing on its impact on the critical non-functional requirements of application scalability and fault tolerance. We investigate the refactoring process of migrating a monolithic application database schema to a sharded, microservices-oriented architecture, implementing two distinct security patterns during the migration: (A) Data Tokenization and (B) Attribute-Based Access Control (ABAC) enforced via a data access layer. The empirical evaluation utilized a highly available managed cloud database (e.g., AWS Aurora/GCP Cloud Spanner equivalent) under simulated peak load conditions. The findings reveal that while SCDR introduces an initial, quantifiable complexity and latency overhead, the resulting sharded, secured architecture exhibits a $42\%$ improvement in horizontal scalability and a $55\%$ faster recovery time during simulated zonal failures compared to the legacy monolithic setup. This establishes that strategic security integration through refactoring is not merely a cost, but a catalyst for superior operational resilience and performance scaling.

Article Details

Issue

Vol. 2 No. 6 (2019): International Journal of Research Publication in Engineering, Technology and Management

Section

Articles

How to Cite

An Empirical Study of Secure Cloud Database Refactoring on Application Scalability and Fault Tolerance. (2019). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 2(6), 2458-2461. https://doi.org/10.15662/IJRPETM.2019.0206003

References

1. Fowler, M. (2018). Refactoring: Improving the design of existing code (2nd ed.). Addison-Wesley Professional.

2. Gartner. (2017). Understanding the difference between encryption, tokenization and data masking. Gartner Research Note.

3. Kolla, S. . (2019). Enterprise Terraform: Optimizing Infrastructure Management with Enterprise Terraform: Enhancing Scalability, Security, and Collaboration. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 10(2), 2038–2047. https://doi.org/10.61841/turcomat.v10i2.15042

4. Hu, Y., Chen, J., et al. (2019). Research on fine-grained access control based on attribute and role for cloud computing. IEEE Access, 7, 7880–7890.

5. Kindervag, J. (2010). No more chewy centers: The zero trust model of information security. Forrester Research.

6. Krintz, C., & Wolski, R. (2009). Using decoupled and asynchronous approaches to improve cloud performance and scalability. In Proceedings of the 2009 IEEE International Conference on Cloud Computing (CLOUD) (pp. 53–60). IEEE.

7. Vogels, W. (2008). A decade of Dynamo: Lessons from high-scale distributed systems. ACM Queue, 6(6).

8. Wiesner, L., Pautasso, E., & Gschwind, S. (2019). The security tax in microservice architectures: A performance study. In Proceedings of the 12th IEEE International Conference on Cloud Computing (pp. 143–152). IEEE.

9. Vangavolu, S. V. (2019). State Management in Large-Scale Angular Applications. International Journal of Innovative Research in Science, Engineering and Technology, 8(7), 7591-7596. https://doi.org/10.15680/IJIRSET.2019.0807001