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              <text>Streamlined Deployment and Monitoring of Cloud-Native Applications on AWS with Kubernetes Prometheus Grafana</text>
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              <text>ArgoCD; AWS; EC2; EKS; Grafana; Helm; Keda; Kubernetes; Prometheus</text>
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              <text>As organizations increasingly move their applications to the cloud, it becomes essential to have an efficient and cost-effective method for deploying and managing those applications. Manual deployment can be time-consuming, error-prone, and expensive. Additionally, managing logs and monitoring resources for each deployment can lead to even greater costs. To address these challenges, we propose implementing an automation strategy for deployment in the cloud. With automation, the deployment process can be streamlined and standardized across different cloud providers, reducing the potential for errors and saving time and resources. Furthermore, a central log system can be implemented to manage logs from different deployments in one location. This provides a unified view of all logs and allows for easier troubleshooting and analysis. Automation can also be used to set up monitoring resources, such as alerts and dashboards, across different deployments. Overall, implementing an automation strategy for deployment in the cloud can help organizations save time and resources while improving their ability to manage and monitor their applications. A centralized log management system can further enhance these benefits by providing a unified view of logs from all deployments  2023 IEEE.</text>
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              <text>Abirami T.; Mapari S.; Jayadharshini P.; Krishnasamy L.; Vigneshwaran R.R.</text>
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              <text>2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023, pp. 1149-1155.</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICAICCIT60255.2023.10465818" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICAICCIT60255.2023.10465818&lt;/a&gt;
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              <text>ISBN: 979-835034438-7</text>
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              <text>Abirami T., Kongu Engineering College, Department of Information Technology, Erode, Tamilnadu, Perundurai, India; Mapari S., Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International, Pune, India; Jayadharshini P., Kongu Engineering College, Department of Artificial Intelligence, Erode, Tamilnadu, Perundurai, India; Krishnasamy L., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bangalore, Kengeri, India; Vigneshwaran R.R., Kongu Engineering College, Department of Information Technology, Erode, Tamilnadu, Perundurai, India</text>
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