Open Access Open Access  Restricted Access Subscription Access

K8MONITOR: CLOUD-NATIVE APP MONITORING

Mulge Bhagya Sree, Mr. R. Mohan Krishna, Dr. P. Shyam Sunder, Dr. K. Rajitha

Abstract


This paper presents K8Monitor, a lightweight, Kubernetes-native system monitoring application designed to offer real-time observability of containerized workloads.   Developed

using Flask, Plotly, psutil,                   and the Kubernetes Python client, K8Monitor is containerized with Docker and deployed on Azure Kubernetes Service (AKS), with container images managed through Azure Container Registry (ACR). The system enables users to monitor CPU and memory utilization of running pods via an interactive web-based dashboard. Resource metrics are collected using psutil and rendered dynamically using Plotly, providing a responsive and user-friendly interface. The backend is implemented in Flask and exposes RESTful APIs to bridge the monitoring logic with the visualization layer. By leveraging the Kubernetes Python client, the system accesses pod-level resource data directly from the cluster, eliminating the need for third-party monitoring agents or external databases. This design ensures a lightweight footprint and facilitates easy deployment.

K8Monitor adopts a modular and secure architecture, enabling periodic metric polling and consistent real-time updates without introducing infrastructure complexity. Unlike conventional tools such as Prometheus or Grafana, K8Monitor prioritizes simplicity, maintainability, and ease of use—making it particularly well- suited for academic, small-scale, and instructional deployments.The proposed system enhances developer understanding of cloud-native technologies, offering practical experience in container orchestration, system monitoring, and real-time data visualization within Kubernetes environments.

Full Text:

PDF

References


A. Nurul Huda & S. S. Kusumawardani, “Kubernetes Cluster Management for Cloud Computing Platform: A Systematic Literature Review,” Jurnal Teknologi Informasi dan Terapan (JUTI), 2023.

Available: https://juti.if.its.ac.id/index.p hp/juti/article/view/1103

V. Medel et al., “Modelling Performance & Resource Management in Kubernetes,” ACM Digital Library, 2016. Available: https://dl.acm.org/doi/10.114 5/2996890.3007869

R. S. Hadikusuma, L. Octavianus Bachri, “Optimization and Monitoring of Kubernetes Cluster Using Various Approaches,” Sinkron: Jurnal dan Penelitian Teknik Informatika, 2022. Available: https://jurnal.polgan.ac.id/ind ex.php/sinkron/article/view/12424

D. Bharadwaj & B. S. Premananda, “Transition of Cloud Computing from Traditional Applications to the Cloud Native Approach,” IEEE Access, 2022. Available: https://doi.org/10.1109/NKC on56289.2022.10126871

Q. Jiao, B. Xu, Y. Fan, “Design of Cloud Native Application Architecture Based on Kubernetes,” IEEE Access, 2021.

Available: https://doi.org/10.1109/DAS C-PICom-CBDCom-

CyberSciTech52372.2021.00088

S. Deng et al., “Cloud-Native Computing: A Survey from the Perspective of Services,” arXiv, 2023. Available: https://arxiv.org/abs/2306.14 402

N. An, “A Customized Kubernetes Scheduling Algorithm to Improve Resource Utilization of Nodes,” IEEE, 2023.

Available: https://www.computer.org/cs dl/proceedings- article/acctcs/2023/108000a588/1NPZ3p XebTy

D. R. Chittibala, “Security in Kubernetes: A Comprehensive Review of Best Practices,” ResearchGate, 2020. Available: https://www.researchgate.net/ publication/379075922


Refbacks

  • There are currently no refbacks.