AI-Augmented Cloud Performance Metrics with Integrated Caching and Transaction Analytics for Superior Project Monitoring and Quality Assurance
DOI:
https://doi.org/10.15662/IJEETR.2022.0406005Keywords:
AI-driven monitoring, cloud performance metrics, adaptive caching, transaction analytics, real-time project tracking, quality assurance, predictive optimization, distributed systemsAbstract
The increasing complexity of modern digital ecosystems demands intelligent, scalable, and real-time monitoring solutions capable of ensuring service reliability, operational transparency, and high-quality project outcomes. This paper presents an AI-augmented cloud performance metrics framework that seamlessly integrates adaptive caching and transaction analytics to enhance project monitoring and quality assurance. The proposed architecture leverages machine learning–driven anomaly detection, predictive resource optimization, and dynamic caching strategies to reduce latency and improve data consistency across distributed cloud environments. Transaction analytics are incorporated to evaluate integrity, throughput, and risk factors, enabling early detection of process deviations and performance bottlenecks. Real-time dashboards and automated quality assessment indicators further empower decision-makers with actionable insights for continuous improvement. Experimental evaluations demonstrate that the integrated AI–cloud framework significantly enhances responsiveness, accuracy, and reliability compared to traditional cloud monitoring systems, making it a robust approach for high-demand project management environments.
References
1. Basili, V. R., Caldiera, G., & Rombach, H. D. (1994). The Goal Question Metric Approach. University of Maryland, Computer Science Technical Report. Computer Science at UMD+2SciSpace+2
2. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2021). The evolution of software maintenance. Journal of Computer Science Applications and Information Technology, 6(1), 1–8. https://doi.org/10.15226/2474-9257/6/1/00150
3. Solingen, R. van, & Berghout, E. (1999). The Goal/Question/Metric Method: A Practical Guide for Quality Improvement of Software Development. McGraw Hill. ResearchGate
4. ISO/IEC 9126 3 (2003). Software engineering — Product quality — Part 3: Internal metrics. ISO. ISO
5. Al Qutaish, R. E., & Al Qutaish, H. (2006). Mapping Between ISO 9126 on Software Product Quality and ISO 14598 on Software Product Evaluation. Proceedings of ACIT2006. acit2k.org
6. Bautista, L., Abran, A., & April, A. (2012). Design of a Performance Measurement Framework for Cloud Computing. Journal of Software Engineering and Applications, 5(2), 69–75. Scientific Research Publishing
7. Mohile, A. (2021). Performance Optimization in Global Content Delivery Networks using Intelligent Caching and Routing Algorithms. International Journal of Research and Applied Innovations, 4(2), 4904-4912.
8. Villalpando, L. E., April, A., & Abran, A. (2013). Performance analysis model for big data applications in cloud computing. Journal of Big Data & Quality, using ISO 25010 quality model. DNB Portal
9. Muthirevula, G. R., Kotapati, V. B. R., & Ponnoju, S. C. (2020). Contract Insightor: LLM-Generated Legal Briefs with Clause-Level Risk Scoring. European Journal of Quantum Computing and Intelligent Agents, 4, 1-31.
10. Kumar, S. N. P. (2022). Improving Fraud Detection in Credit Card Transactions Using Autoencoders and Deep Neural Networks (Doctoral dissertation, The George Washington University).
11. França, J. M., & colleagues. (2015). Quality Model for SOA Applications Based on ISO 25010. Proceedings of SCITEPRESS. ScitePress
12. Blas, M. J., Herrero, P., & others. (2020). Modeling and simulation framework for quality estimation of cloud applications using ISO/IEC 25010 quality model. SN Applied Sciences. SpringerLink
13. Wagner, S., Lochmann, K., Kläs, M., Trendowicz, A., Plösch, R., Seidl, A., & Goeb, A. (2016). The Quamoco Product Quality Modelling and Assessment Approach. arXiv preprint. arXiv
14. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.
15. Thangavelu, K., Keezhadath, A. A., & Selvaraj, A. (2022). AI-Powered Log Analysis for Proactive Threat Detection in Enterprise Networks. Essex Journal of AI Ethics and Responsible Innovation, 2, 33-66.
16. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
17. Herbst, N., Krebs, R., Oikonomou, G., Evangelinou, A., Iosup, A., & Kounev, S. (2016). Ready for Rain? A View from SPEC Research on the Future of Cloud Metrics. arXiv preprint. arXiv
