Leveraging AWS CloudWatch, Nagios, and Splunk for Real-Time Cloud Observability

The article examines the importance of monitoring cloud infrastructure and capabilities of AWS CloudWatch, Nagios and Splunk in providing real-time views of operations. According to the survey of enterprises that are shifting toward flexible, scalable and economical cloud architecture, there is a need to ensure consistent performance, availability, and security of cloud workloads. Such continuous, real-time monitoring allow proactive notification and corrective actioning of performance bottlenecks, security incidents and service downages, prior to their access to end-users. The paper will analyze the functional scope and architectural strength of every tool and the deployment constraints of each tool. The AWS CloudWatch is highly integrated with AWS services and has a broad level of metrics and automated alarms and log analytics on cloud-native workloads. Being an open-source solution, Nagios allows configuration of its monitoring capabilities and an easy integration with hybrid and multi-cloud platforms. Splunk has proven to be feasible because it has high rates of real-time log ingestion, ability to conduct advanced analytics, and predictive modeling using built-in machine learning algorithms. Comparative analysis draws attention to the fact that, despite certain similarities, each of the platforms can serve the various observed strategies, which enables organizations to choose an effective monitoring stack based on their cloud service model. The paper is also focused on best practices: standardization of metrics, anomaly detection, and alert optimization, and emergent trends, such as self-healing infrastructure and observability pipelines with AI. With the strategic monitoring and the usage of tool specific features, organizations are able to achieve operational resilience, high availability, and compliance readiness in the new age cloud based context.

Authors

Publication Details

Journal
International Journal of Computational and Experimental Science and Engineering
Published
2025-08-29
DOI
https://doi.org/10.22399/ijcesen.3781
Citations
1
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
3.25
Controls
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article

Leveraging AWS CloudWatch, Nagios, and Splunk for Real-Time Cloud Observability

Naga Murali Krishna Koneru
1 citations
International Journal of Computational and Experimental Science and Engineering
Cloud Computing and Resource Management
3.25
article

Leveraging AWS CloudWatch, Nagios, and Splunk for Real-Time Cloud Observability

Naga Murali Krishna Koneru
article en
1 citations

Abstract

The article examines the importance of monitoring cloud infrastructure and capabilities of AWS CloudWatch, Nagios and Splunk in providing real-time views of operations. According to the survey of enterprises that are shifting toward flexible, scalable and economical cloud architecture, there is a need to ensure consistent performance, availability, and security of cloud workloads. Such continuous, real-time monitoring allow proactive notification and corrective actioning of performance bottlenecks, security incidents and service downages, prior to their access to end-users. The paper will analyze the functional scope and architectural strength of every tool and the deployment constraints of each tool. The AWS CloudWatch is highly integrated with AWS services and has a broad level of metrics and automated alarms and log analytics on cloud-native workloads. Being an open-source solution, Nagios allows configuration of its monitoring capabilities and an easy integration with hybrid and multi-cloud platforms. Splunk has proven to be feasible because it has high rates of real-time log ingestion, ability to conduct advanced analytics, and predictive modeling using built-in machine learning algorithms. Comparative analysis draws attention to the fact that, despite certain similarities, each of the platforms can serve the various observed strategies, which enables organizations to choose an effective monitoring stack based on their cloud service model. The paper is also focused on best practices: standardization of metrics, anomaly detection, and alert optimization, and emergent trends, such as self-healing infrastructure and observability pipelines with AI. With the strategic monitoring and the usage of tool specific features, organizations are able to achieve operational resilience, high availability, and compliance readiness in the new age cloud based context.

International Journal of Computational and Experimental Science and EngineeringVol. 11(3)
Openalex Percentile: Top 7%
Cloud Computing and Resource Management
3.25
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