ENTERPRISE INFRASTRUCTURE AUTOMATION

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Introduction

This case study is a deep dive into the implementation of network automation mechanisms to streamline network management tasks, enhance operational efficiency, and improve overall network performance. Python scripts are written and packaged using PIP for executing quick tasks network-specific tasks, while workflows automate complex actions such as querying network management servers for packet loss metrics. Additionally, log collection using Kibana and alarming mechanisms further enhance network monitoring capabilities.

Situation

The customer, a growing technology firm, had a complex network infrastructure consisting of multiple switches, routers, and servers. The company's network management processes are time-consuming and error-prone, leading to operational inefficiencies and frequent manual intervention for routine tasks. Network changes, such as port shutdowns or modifying BGP path preferences, require manual configurations, leading to delays and potential human errors. Additionally, monitoring and troubleshooting network issues are challenging due to the lack of centralized log collection and alarming mechanisms. Keep reading to see how we helped the customer streamline and automate their network operation tasks.

Solutions

Implement workflow automation: We created Python-based workflows to perform complex actions, such as querying the network management server for packet loss metrics. These workflows enhance network monitoring and troubleshooting capabilities, reducing manual intervention.
Automate network tasks. Python scripts are developed to handle specific network tasks, such as port shutdown and modifying BGP MED values. These scripts are packaged using PIP, enabling easy installation and distribution across the network infrastructure.
Log Collection and Analysis: Kibana, a log management and analysis tool, was utilized to collect device logs from various network components. These logs provide valuable insights into network behavior, aiding in performance optimization and issue resolution.
Alarming mechanisms were used to generate tickets based on predefined log events.
Achieve cost savings, resource optimization, and improved network performance.

Implementation

Phase 1: Assessment and Planning:
Evaluate existing network infrastructure and identify automation opportunities.
Define project scope, objectives, and success criteria.
Assess resource requirements, including personnel, hardware, and software.
Phase 2: Development and Testing:
Develop Python scripts to automate network tasks: We wrote and packaged Python scripts for executing most network tasks such as shut a port on a router or restart the NIC on a server. This script conducted pre and post checks to ensure that there is no adverse effect resulting from it.
Create workflows using Python-based automation frameworks: We wrote workflows to carry out more complex tasks such as deploying a new device, collecting packet loss metrics of a device from the NMS server.
Configure log collection and analysis: Kibana was used to design a log collection pipeline that collected data from network devices, parses and indexes the logs, stores the logs in Elasticsearch, analyzes and visualizes the logs and then alerts the network owner when a pre-configured alarm threshold is breached.
Conduct thorough testing and validation of all implemented components: EVE-NG Pro was used to simulate customer's environment. Thorough testing was done on this simulation to ensure that scripts worked as expected. Demo was presented to the customer using this simulation.
Phase 3: Deployment and Integration:
Deploy Python scripts and workflows across the network infrastructure.
Integrate automation mechanisms with existing network management systems.
Configure and fine-tune log collection and analysis processes.
Implement alarming mechanisms to generate tickets or notifications for critical events.
Provide training and documentation to network administrators for ongoing maintenance and support.

Cost Savings and Project Results

The customer was able to save cost in the following areas:

Reduction in manual network management tasks, leading to decreased operational expenses.
Increased efficiency and productivity of network administrators, allowing them to focus on higher-value activities.
Improved network performance and reliability, minimizing downtime and associated costs.
Enhanced scalability and agility, facilitating faster network expansion and adaptation to changing requirements

Benefits and Results:

Streamlined network management processes, reducing human errors and manual intervention.
Faster response times to network incidents and improved troubleshooting capabilities.
Enhanced network visibility through log collection and analysis.
Proactive identification and resolution of network issues using alarming mechanisms.
Cost savings through automation, reduced downtime, and optimized resource utilization.

Conclusion

The implementation of network automation mechanisms, including Python scripting, workflow automation, log collection and analysis, and alarming mechanisms, has transformed the formerly non-automated network into an efficient and cost-effective infrastructure. The project has achieved improved operational efficiency, reduced downtime, and enhanced network performance, leading to significant benefits