Adebayo Omolumo
Software Engineer
Cloud | DevOps | Web(3)
  • Location:
  • Type:
    Cloud Engineering
  • Period:
    August - September 2021
Azure Cloud
  • Azure Kubernetes Service (AKS)
  • Virtual Machine Scale Sets
  • Application Insights
  • Azure Log Analytics
  • Azure Runbooks
  • Performance Data
  • Health Data
  • Monitoring Solutions
  • Auto-Scaling
  • Azure Automation
  • Runbook Automation
  • Alert Configuration
  • Telemetry Data
  • Remediation Tasks
  • Dynamic Scaling
  • Automated Issue Resolution
  • Application Performance
  • Proactive Response

Cloud Infrastructure Monitoring and Automation

Analytics, Azure Cloud

Project details

Project Description:

As part of a training initiative at my company, I engaged in a hands-on project focused on demonstrating proficiency in collecting, analyzing, and acting upon performance and health data within a cloud environment. The project emphasized the application of cloud technologies, including Azure Kubernetes Service (AKS), Virtual Machine Scale Sets (VMSS), Application Insights, Azure Log Analytics, and Azure Runbooks, to effectively diagnose, resolve, and automate application and infrastructure issues.

Problem Statement:

While collecting performance and health data is crucial, the real challenge lies in leveraging this data to make informed decisions and automating remediation tasks. This project aimed to showcase skills in implementing comprehensive monitoring solutions and creating automated resolution mechanisms for application and infrastructure issues.


  • Application Insights Implementation on VMSS:
    I set up Application Insights on a Virtual Machine Scale Set (VMSS) and integrated monitoring within an application to collect telemetry data, including metrics and traces.
  • Auto-Scaling Configuration:
    I configured auto-scaling for the VMSS based on the collected telemetry data, ensuring the application could dynamically adapt to varying loads.
  • Azure Automation Account and RunBook Creation:
    I established an Azure Automation account and developed a custom RunBook to automate the resolution of performance issues. This RunBook would execute predefined remediation steps in response to specific triggers.
  • Alert Configuration:
    I configured alerts to monitor the performance of an AKS cluster. When thresholds were exceeded, the alerts triggered auto-scaling actions and also initiated the execution of the RunBook to resolve the issue.

Outcome of the Project:

  • Comprehensive Monitoring:
    By implementing Application Insights and other monitoring solutions, the project demonstrated the ability to collect detailed telemetry data for analysis.
  • Dynamic Scaling:
    The auto-scaling setup ensured that the VMSS could seamlessly adjust its capacity based on real-time load, enhancing application performance and user experience.
  • Automated Issue Resolution:
    The Azure Automation RunBook showcased the capability to automate the resolution of performance issues without manual intervention, leading to quicker problem-solving.
  • Effective Alerting:
    The configured alerts enabled proactive responses to performance anomalies, triggering auto-scaling and automated remediation to maintain optimal application performance.
  • Cloud Expertise:
    The project underscored proficiency in cloud technologies, showcasing my ability to design and implement complex solutions in a cloud environment.

Key Takeaways

In conclusion, the "Cloud Infrastructure Monitoring and Automation" project highlighted my ability to navigate the challenges of monitoring, analyzing, and automating cloud-based applications and infrastructure. By utilizing a variety of Azure services and developing customized solutions, I demonstrated a comprehensive skill set that aligns with the demands of modern cloud engineering.


The "Cloud Infrastructure Monitoring and Automation" project was undertaken as part of a training initiative, focusing on proficiency in collecting, analyzing, and acting upon performance and health data within a cloud environment. It utilized various Azure services and technologies, including Azure Kubernetes Service (AKS), Virtual Machine Scale Sets (VMSS), Application Insights, Azure Log Analytics, and Azure Runbooks. The project addressed the challenges of effective data utilization, automation of issue resolution, and comprehensive monitoring


Azure Kubernetes Service (AKS)
AKS is a managed container orchestration service provided by Microsoft Azure. It simplifies the deployment, management, and scaling of containerized applications using Kubernetes.
AKS was used to host containerized applications and provided the infrastructure for dynamic scaling in response to performance data.
Virtual Machine Scale Sets (VMSS)
VMSS is an Azure service that allows for the deployment and management of a group of identical virtual machines. It offers high availability to your applications, and it can automatically increase or decrease the number of VM instances.
VMSS was employed for hosting applications, and Application Insights was integrated to collect telemetry data for monitoring.
Application Insights
Application Insights is an Azure service designed for application performance monitoring. It offers features for collecting telemetry data, detecting performance anomalies, and gaining insights into application behavior.
Application Insights was implemented on VMSS to collect telemetry data, including metrics and traces, for in-depth monitoring of application performance.
Azure Log Analytics
Azure Log Analytics is a service that collects and analyzes data from a variety of sources, including VMs and applications. It provides insights into system and application performance.
Azure Log Analytics likely played a role in aggregating and analyzing log data to identify performance issues and trigger alerts.
Azure Automation Account and RunBooks
Azure Automation allows for the creation of RunBooks, which are automation scripts that perform actions in response to specified triggers.
An Azure Automation account was established, and custom RunBooks were created to automate the resolution of performance issues based on specific triggers.
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Adebayo Omolumo