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Optimize and manage Linux-based Azure VMs

Mastery

Article from ADMIN 81/2024

By Marcin Gastol

Master advanced configuration techniques for Azure virtual machines on Linux with a focus on optimizing performance by applying system tweaks, managing storage solutions, and automating monitoring tasks.

In the realm of cloud computing, the performance of your virtual machines (VMs) directly affects the efficiency, reliability, and cost-effectiveness of your services, especially in Microsoft Azure, where Linux VMs are often deployed to run various applications – from web services to data processing tasks. System optimization for Azure VMs running Linux is not just a routine task; it's a critical endeavor that ensures your workloads perform optimally, making the best use of resources while controlling costs.

Assessing System Performance

In the pursuit of optimizing Azure VMs on Linux, it is essential to grasp and utilize the correct tools and metrics to assess system performance effectively. These tools are pivotal in pinpointing performance bottlenecks and are integral to the ongoing process of monitoring and adjustment.

Azure Monitor stands out as a robust built-in service within Azure for gathering, analyzing, and acting on telemetry data from Azure and on-premises environments. It offers a comprehensive suite of metrics and logs, facilitating a deep understanding of application and resource behavior on Azure. Features such as alert setups, metric visualization in dashboards, and detailed analyses are invaluable for pinpointing optimization opportunities.

For Unix systems, interactive process viewers like the more traditional tool, top, and htop, known for its user-friendly interface and additional functionalities like vertical and horizontal scrolling, are essential for real-time monitoring of system resources such as CPU, memory, and process management.

The vmstat command is another vital tool, offering snapshots of processes, memory, paging, block I/O, traps, and CPU activity. This tool is especially useful for real-time system monitoring, providing essential data on the system's key resources and performance.

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