Authors : Shankar Dheeraj Konidena

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://tinyurl.com/2dew74yt

Scribd : https://tinyurl.com/29y434ue

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG641

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This research article explores strategies for cost-effective scalability in cloud monitoring systems. As the complexity and scale of IT-based infrastructure are growing, an efficient monitoring system becomes vital for maintaining performance and optimizing resources while managing costs. The current study aims to shed light on various approaches to cost-effective scalability by considering factors such as data collection methods, storage optimization, and adaptive monitoring techniques. A comparison between cloud monitoring tools, Amazon Cloud Watch and Datadog, has been made to gain a better understanding of the monitoring tools. The research indicates that a multi-faceted approach is necessary for cost-effective scalability in cloud monitoring systems, and there must be a holistic approach in the selection of cloud monitoring tools, depending on the organization's requirements. In the later sections, strategies, like distributed data collection, hierarchical aggregation, adaptive sampling, and machine learning- based predictive scaling, can significantly improve monitoring system scalability while optimizing resource utilization.

Keywords : Cloud Computing, Cost-Effective Scalability, AI & ML, Cloud Monitoring Tools.

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