Leveraging AWS CloudWatch, Nagios, and Splunk for Real-Time Cloud Observability
DOI:
https://doi.org/10.22399/ijcesen.3781Keywords:
Cloud Infrastructure Monitoring, AWS CloudWatch, Nagios, Splunk, Real-Time Monitoring, Cloud Performance, Machine LearningAbstract
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.
References
[1] Aceto, G., Botta, A., De Donato, W., & Pescapè, A. (2013). Cloud monitoring: A survey. Computer Networks, 57(9), 2093-2115.
[2] Al Said, T. (2016). Enhancing security in public IaaS cloud systems through VM monitoring: a consumer’s perspective (Doctoral dissertation, Cardiff University).
[3] Barth, W. (2008). Nagios: System and network monitoring. No Starch Press.
[4] Chavan, A. (2021). Exploring event-driven architecture in microservices: Patterns, pitfalls, and best practices. International Journal of Software and Research Analysis. https://ijsra.net/content/exploring-event-driven-architecture-microservices-patterns-pitfalls-and-best-practices
[5] Chavan, A. (2024). Fault-tolerant event-driven systems: Techniques and best practices. Journal of Engineering and Applied Sciences Technology, 6, E167. http://doi.org/10.47363/JEAST/2024(6)E167
[6] Dhanagari, M. R. (2024). MongoDB and data consistency: Bridging the gap between performance and reliability. Journal of Computer Science and Technology Studies, 6(2), 183-198. https://doi.org/10.32996/jcsts.2024.6.2.21
[7] Dhanagari, M. R. (2024). Scaling with MongoDB: Solutions for handling big data in real-time. Journal of Computer Science and Technology Studies, 6(5), 246-264. https://doi.org/10.32996/jcsts.2024.6.5.20
[8] Farshchi, M., Schneider, J. G., Weber, I., & Grundy, J. (2018). Metric selection and anomaly detection for cloud operations using log and metric correlation analysis. Journal of Systems and Software, 137, 531-549.
[9] Galletta, D. F., Henry, R., McCoy, S., & Polak, P. (2004). Web site delays: How tolerant are users?. Journal of the Association for Information Systems, 5(1), 1-28.
[10] Garland, K. J., & Noyes, J. M. (2004). Computer experience: a poor predictor of computer attitudes. Computers in Human Behavior, 20(6), 823-840.
[11] Grossi, G., Reichard, C., Thomasson, A., & Vakkuri, J. (2017). Theme: performance measurement of hybrid organizations–emerging issues and future research perspectives. Public Money and Management, 37(6), 379-386.
[12] Josephsen, D. (2007). Building a monitoring infrastructure with Nagios. Prentice Hall PTR.
[13] Konneru, N. M. K. (2021). Integrating security into CI/CD pipelines: A DevSecOps approach with SAST, DAST, and SCA tools. International Journal of Science and Research Archive. https://ijsra.net/content/role-notification-scheduling-improving-patient
[14] Lee, J. B., Yoo, T. H., Lee, E. H., Hwang, B. H., Ahn, S. W., & Cho, C. H. (2021). High-performance software load balancer for cloud-native architecture. IEEE Access, 9, 123704-123716.
[15] Lingamallu, P. K., & Oliveira, F. (2023). AWS Observability Handbook: Monitor, trace, and alert your cloud applications with AWS'myriad observability tools. Packt Publishing Ltd.
[16] Liu, C. H., & Chen, W. H. (2019). The study of using big data analysis to detecting APT attack. Journal of Computers, 30(1), 206-222. Barker, R. (2020). The uses and benefits of Splunk in continuous integration.
[17] Méndez Roca, M. (2020). New Innovations in eIDAS-compliant Trust Services: Anomaly detection on log data (Master's thesis, Universitat Politecnica de Catalunya).
[18] Mongkolluksamee, S., Pongpaibool, P., & Issariyapat, C. (2010, May). Strengths and limitations of Nagios as a network monitoring solution. In Proceedings of the 7th International Joint Conference on Computer Science and Software Engineering (JCSSE 2010). Bangkok, Thailand (pp. 96-101).
[19] Nikkhouy, E. (2016). Monitoring Service Chains in the Cloud.
[20] Parikh, A. (2019). Cloud security and platform thinking: an analysis of Cisco Umbrella, a cloud-delivered enterprise security (Doctoral dissertation, Massachusetts Institute of Technology).
[21] Poornalinga, K. S., & Rajkumar, P. (2016). Continuous integration, deployment and delivery automation in AWS cloud infrastructure. Int. Res. J. Eng. Technol.
[22] Pourmajidi, W., Steinbacher, J., Erwin, T., & Miranskyy, A. (2018). On challenges of cloud monitoring. arXiv preprint arXiv:1806.05914.
[23] Raj, P., Raman, A., Nagaraj, D., Duggirala, S., Raj, P., Raman, A., ... & Duggirala, S. (2015). Real-Time Analytics Using High-Performance Computing. High-Performance Big-Data Analytics: Computing Systems and Approaches, 161-185.
[24] Sandhu, R. S., & Samarati, P. (1997). Authentication, Access Controls, and Intrusion Detection. The Computer Science and Engineering Handbook, 1, 929-1.
[25] Sardana, J. (2022). Scalable systems for healthcare communication: A design perspective. International Journal of Science and Research Archive. https://doi.org/10.30574/ijsra.2022.7.2.0253
[26] Sardana, J. (2022). The role of notification scheduling in improving patient outcomes. International Journal of Science and Research Archive. https://ijsra.net/content/role-notification-scheduling-improving-patient
[27] Singh, V. (2022). Visual question answering using transformer architectures: Applying transformer models to improve performance in VQA tasks. Journal of Artificial Intelligence and Cognitive Computing, 1(E228). https://doi.org/10.47363/JAICC/2022(1)E228
[28] Singh, V. (2023). Enhancing object detection with self-supervised learning: Improving object detection algorithms using unlabeled data through self-supervised techniques. International Journal of Advanced Engineering and Technology. https://romanpub.com/resources/Vol%205%20%2C%20No%201%20-%2023.pdf
[29] Solis Patrón, C. Y. (2015). Data Analytics as a Service: A look inside the PANACEA project (Master's thesis, Universitat Politècnica de Catalunya).
[30] Sommer, P. (1999). Intrusion detection systems as evidence. Computer Networks, 31(23-24), 2477-2487.
[31] Stephen, A., Benedict, S., & Kumar, R. A. (2019). Monitoring IaaS using various cloud monitors. Cluster Computing, 22(Suppl 5), 12459-12471.
[32] Verginadis, Y. (2023, March). A review of monitoring probes for cloud computing continuum. In International Conference on Advanced Information Networking and Applications (pp. 631-643). Cham: Springer International Publishing.
[33] Ward, J. S., & Barker, A. (2014). Observing the clouds: a survey and taxonomy of cloud monitoring. Journal of Cloud Computing, 3, 1-30.
[34] Weinman, J. (2012). Cloudonomics: The business value of cloud computing. John Wiley & Sons.
[35] Zadrozny, P., & Kodali, R. (2013). Big data analytics using Splunk: Deriving operational intelligence from social media, machine data, existing data warehouses, and other real-time streaming sources. Apress.
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