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Golden Path Engineering Platform for Standardized Development of Containerized Telecommunication Network Monitoring Applications

Description

This project develops a Golden Path Engineering Platform that provides a standardized and automated approach for developing containerized telecommunication network monitoring applications. The platform gives developers a common workflow for building, testing, packaging, and deploying network monitoring services. The application collects network data, processes it, detects network issues, and presents the results through a monitoring dashboard. This approach reduces manual setup, improves consistency, and makes application development and deployment faster and more reliable.

Aim

To develop a standardized engineering platform that simplifies the development, containerization, testing, and deployment of telecommunication network monitoring applications.

Objectives

01 Create reusable application templates to provide a standard project structure and simplify application setup.
02 Automate application building, testing, and packaging using a standardized container-based development pipeline.
03 Provide standardized container configurations so network monitoring applications can run consistently across different environments.
04 Automate application deployment to reduce manual deployment steps and configuration errors.
05 Provide standard monitoring and logging for applications developed through the platform.
06 Simplify developer workflows by allowing developers to create and deploy network monitoring applications using predefined development patterns.

Application Workflow

01

Stage 1. Collect Network Data

Process

The application collects real-time data from telecommunication network devices such as routers, switches, and base stations.

Tools
SNMP
Implementation

Python services collect network metrics such as bandwidth usage, latency, packet loss, and device status using SNMP.

02

Stage 2. Process Network Data

Process

The collected network data is cleaned and processed.

Tools
Python
Implementation

The application processes the raw network data and converts it into useful network performance information.

03

Stage 3. Analyze Network Performance

Process

The application analyzes network metrics to determine the health and performance of the network.

Tools
Prometheus
Implementation

Network metrics are evaluated to identify abnormal traffic, high latency, packet loss, or other performance issues.

04

Stage 4. Detect Network Issues

Process

The application identifies network failures or abnormal conditions.

Tools
Prometheus
Implementation

Predefined thresholds and monitoring rules are used to detect issues such as device failures, high packet loss, or excessive network usage.

05

Stage 5. Generate Alerts

Process

The application notifies administrators when a network problem is detected.

Tools
Prometheus Alertmanager
Implementation

Alert rules trigger notifications when network metrics exceed defined thresholds.

06

Stage 6. Display Monitoring Dashboard

Process

Network administrators view the current network condition through a dashboard.

Tools
Grafana
Implementation

The dashboard displays device status, network traffic, latency, packet loss, alerts, and overall network health.

07

Stage 7. Store Historical Data & Reports

Process

The application stores monitoring data for historical analysis and reporting.

Tools
Prometheus PostgreSQL
Implementation

Historical network information is stored and used to analyze network performance trends and generate reports.

Cloud Infrastructure and Tools

Self-Service Scaffolding Portal Backstage Software Templates

Provides a web portal where developers can select approved templates and quickly create new telecommunication network monitoring projects with the required application code and configuration.

Cloud-Native Pipeline Engine Tekton Pipelines

Automates the application build, testing, and container image creation process. It runs these tasks inside Kubernetes so developers do not need to perform them manually.

Distributed Observability Engine OpenTelemetry

Collects application telemetry such as metrics, logs, and traces in a standard way. It helps the development team monitor application performance and identify problems.

Container Orchestration Substrate Kubernetes

Runs and manages the containerized network monitoring application. It handles scheduling, scaling, networking, and restarting failed containers.

Microservices Container Platform Docker

Packages the network monitoring application, libraries, and dependencies into containers so it can run consistently across environments.

Target Compute Infrastructure Cloud EC2 Instances

Provides virtual servers used to run the self-managed Kubernetes cluster, platform components, and network monitoring application workloads.

Server Operating System Substrate Ubuntu Server 24.04 LTS

Provides the Linux operating system for the EC2 servers and gives the Kubernetes nodes a consistent environment.

Target Cloud Networking Plane Cloud VPC

Provides the private cloud network where the Kubernetes cluster, platform components, and application workloads run securely.

Security Controls & Firewalls Cloud Security Groups + Network ACLs

Control network traffic and protect the Kubernetes nodes, platform components, and application services from unauthorized access.

Target Cloud Storage Interface Cloud EBS

Provides persistent block storage for EC2 instances and Kubernetes workloads that require persistent storage, such as application data and logs.

Network Monitoring & Metrics Prometheus

Collects and stores network and application metrics such as device status, bandwidth, latency, and packet loss.

Network Data Collection SNMP

Allows the application to collect monitoring information from network devices such as routers, switches, and telecom equipment.

Monitoring Dashboard Grafana

Displays network metrics, device health, traffic, latency, packet loss, and alerts through monitoring dashboards.

Implementation Process

01
Step 1 – Define the Golden Path
  • Review existing telecommunication network monitoring applications and identify common development requirements.
  • Define a standard project structure for network monitoring applications.
  • Create Backstage Software Templates for generating new projects.
  • Define standard application, container, deployment, and monitoring configurations.
  • Document the approved development and deployment workflow for developers.
02
Step 2 – Set Up Cloud & Kubernetes Infrastructure
  • Create an Cloud VPC for the platform and application environment.
  • Create EC2 instances to host the Kubernetes cluster.
  • Install Ubuntu Server 24.04 LTS on the EC2 instances.
  • Configure Kubernetes control-plane and worker nodes.
  • Configure Security Groups, Network ACLs, and EBS storage.
03
Step 3 – Develop the Network Monitoring Application
  • Develop the application backend using Python.
  • Configure SNMP to collect data from telecom network devices.
  • Process metrics such as bandwidth, latency, packet loss, and device status.
  • Implement logic to detect abnormal network conditions.
  • Create application health checks and required monitoring endpoints.
04
Step 4 – Automate Build, Containerization & Deployment
  • Configure Tekton to automate the application build and testing process.
  • Run automated tests to validate the application.
  • Use Docker to create the application container image.
  • Deploy the containerized application to Kubernetes.
  • Configure Kubernetes services, resources, networking, and environment settings.
05
Step 5 – Implement Monitoring & Validate the System
  • Configure Prometheus to collect application and network metrics.
  • Configure Grafana to display network monitoring dashboards.
  • Configure OpenTelemetry for application logs, metrics, and traces.
  • Test scenarios such as device failure, high latency, packet loss, and network congestion.
  • Verify that issues are detected, alerts are generated, and the dashboard displays the correct network status.

Proposed Solution

The proposed solution provides a Golden Path Engineering Platform that standardizes how telecommunication network monitoring applications are created, tested, containerized, deployed, and monitored. Developers use predefined application templates to create projects with consistent configurations and workflows. The platform automates the application build, testing, containerization, and deployment process, while the monitoring application collects network data, detects network issues, and provides dashboards and alerts.

Benefits

Standardized Development: Provides a consistent structure and workflow for monitoring applications.
Faster Application Setup: Reusable templates reduce the time required to create new projects.
Automated Delivery: Reduces manual effort in building, testing, and deploying applications.
Consistent Environments: Containerization ensures applications run consistently across environments.
Improved Monitoring: Provides centralized metrics, dashboards, logs, and alerts.

Challenges

Template Maintenance: Golden Path templates must be regularly updated and maintained.
Platform Complexity: Managing the platform, Kubernetes, and supporting services can require specialized skills.
Network Integration: Connecting with different telecom devices and network protocols can be challenging.
Monitoring Scale: Large telecom networks can generate significant volumes of monitoring data.
Security Management: Network credentials, device access, and application data must be securely managed.
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