Developing an Innovative Machine Learning Integrated Cloud Monitoring System for Cloud-Based Services


  • Nisha M. Shrirao, Saket Mishra, Raghavendra R., Amandeep Gill, Sachin S. Pund, Shital Barde


Cloud Monitoring System, Cloud infrastructure, Machine Learning, Sea Horse fine-tuned Extreme Gradient Boosting (SH-XGBoost).


A cloud monitoring system is an integrated solution for monitoring as well as managing the performance, availability along with the security of cloud-based infrastructure and services. It employs a variety of tools and methods to gather, analyze and present data on resource use, application activity and user interactions. By monitoring critical parameters in real-time, it allows proactive problem identification and resolution, resource allocation optimization, as well as adherence to service-level agreements. In this research, we developed an innovative machine learning (ML) integrated cloud monitoring system named Sea Horse fine-tuned Extreme Gradient Boosting (SH-XGBoost). Initially, we collected a dataset that includes various types of cloud environment scenarios to train our proposed approach. We utilized the Robust Scaling (RS) algorithm to pre-process the gathered raw data. We employed the Sea Horse Optimization algorithm to enhance the primary characteristics of the proposed XG-Boost algorithm. The suggested approach is implemented in Python software. The finding evaluation phase is performed with multiple metrics such as,F1-score (98.49%), Recall (98.38%), Precision (98.53%) and Accuracy (98.43%) to assess the proposed SH-XGBoost approach with other conventional approaches. The experimental findings illustrate that the proposed SH-XGBoost approach performed better than other existing approaches for novel cloud monitoring systems.


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How to Cite

Saket Mishra, Raghavendra R., Amandeep Gill, Sachin S. Pund, Shital Barde, N. M. S. . (2024). Developing an Innovative Machine Learning Integrated Cloud Monitoring System for Cloud-Based Services. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1058–1066. Retrieved from



Research Article