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2 min read14 weeks

Container Orchestration Dashboard for Microservices

HardBTech CSEMCAMTech CSEBE CSE+1 moreCloud Computing
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A web-based dashboard for monitoring and managing containerized microservices deployed on Kubernetes. The dashboard provides real-time resource utilization, service health checks, log aggregation, and one-click scaling, making Kubernetes accessible to developers without deep DevOps expertise.

Problem Statement

Kubernetes is the industry standard for container orchestration, but its steep learning curve and complex CLI-based management discourage many development teams from adopting it. While tools like Rancher and Lens exist, they are either enterprise-focused or too feature-heavy for small teams. A simplified dashboard focused on essential operations would help small teams adopt Kubernetes more easily.

Objectives

1

Build a real-time Kubernetes cluster monitoring dashboard

2

Implement service deployment and scaling controls

3

Create a centralized log viewer for all services

4

Design health check monitoring with alert notifications

5

Develop resource utilization graphs and trends

Modules

M1

Cluster Overview

Real-time view of nodes, pods, services, and deployments with health status indicators.

M2

Service Management

Deploy, scale, restart, and delete services through a GUI with YAML template support.

M3

Log Viewer

Centralized log aggregation with search, filtering by service, and real-time log streaming.

M4

Monitoring & Alerts

CPU, memory, and network usage graphs with configurable alert thresholds and Slack/email notifications.

M5

User Management

RBAC-based access control for team members with audit logging of all management actions.

Expected Output

A clean, intuitive web dashboard where developers can view their Kubernetes cluster health, monitor resource usage across services, read aggregated logs, scale services up or down with a click, and receive alerts when services are unhealthy or resources are running low.

Future Scope

CI/CD pipeline integration for automated deployments, cost analysis and optimization recommendations, multi-cluster management, GitOps workflow support, and AI-powered anomaly detection in service metrics.

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This project uses: Node.js, React, Python, Docker, Kubernetes. Basic knowledge of these technologies is recommended before starting.