Why does our Azure Function App with an Elastic Premium Plan take 8x longer to run a CPU-intensive .exe than our local machine or Azure App Service web job?

Jay Chotaliya 0 Reputation points
2024-06-06T07:10:23.5566667+00:00

We've created Azure Function using .NET 6.0 Isolated. That function internally executes a .exe process to generate data regarding the application. The .exe file is CPU-intensive and 48MB in size. This .exe takes roughly 1 to 2 minutes to run on both my local machine and an Azure App Service web job.

 
However, when deployed to an Azure Function App with an Elastic Premium Plan, the execution time jumps to approximately 9 minutes.

I'm wondering what could be causing this significant slowdown in the function app despite using a powerful plan.

Azure Functions
Azure Functions
An Azure service that provides an event-driven serverless compute platform.
4,515 questions
ASP.NET Core
ASP.NET Core
A set of technologies in the .NET Framework for building web applications and XML web services.
4,308 questions
Azure App Service
Azure App Service
Azure App Service is a service used to create and deploy scalable, mission-critical web apps.
7,212 questions
0 comments No comments
{count} votes

1 answer

Sort by: Most helpful
  1. innovation gadget 155 Reputation points
    2024-06-06T07:28:56.2533333+00:00

    Hello

    There are several potential factors that might be causing the significant slowdown in execution time of your .exe process when running within an Azure Function App on an Elastic Premium Plan. Here are some areas to investigate and possible solutions:

    1. Resource Constraints and Configuration
    • Scaling and Instances: Ensure your function app is appropriately scaled and has sufficient instances to handle the workload. Check the scaling settings in your Elastic Premium Plan.
    • CPU and Memory Allocation: Verify the CPU and memory allocated to your function app. You might need to adjust the plan or settings to provide more resources to your function.
    1. Sandbox Limitations
    • Azure Functions run in a sandboxed environment with certain limitations compared to a full VM or your local machine. This can affect performance, especially for CPU-intensive tasks.
    • I/O Operations: If your .exe performs significant I/O operations (e.g., reading/writing files), the sandbox environment might impose additional latency.
    1. Networking and Latency
    • Network Overhead: If your .exe process relies on network communication (e.g., accessing external services or databases), network latency in the Azure environment could be a factor.
    • Storage Latency: Ensure that any storage resources your function app interacts with (e.g., Azure Blob Storage) are in the same region as your function app to minimize latency.
    1. Cold Start Times
    • Function Cold Starts: Elastic Premium Plan reduces cold starts but doesn't eliminate them. Cold starts can significantly impact execution time, especially if your .exe process is run at the start of the function execution.
    • Warm-Up: Consider implementing a warm-up trigger to keep your function instances ready to handle requests more quickly.
    1. Process Execution and Management
    • Execution Context: Ensure that the .exe process is managed correctly within the Azure Function context. Incorrect process management might lead to delays.
    • Concurrency and Parallelism: Verify that the function is not running multiple instances of the .exe process concurrently, which might lead to resource contention and increased execution time.
    1. Logging and Monitoring
    • Application Insights: Use Application Insights or other monitoring tools to gather detailed performance metrics and logs. This can help identify bottlenecks or specific points of delay.
    • Custom Logging: Add custom logging around the execution of your .exe process to pinpoint where the delays occur.
    1. Function Code and Dependencies
    • Optimize Code: Ensure your function code is optimized and doesn't have unnecessary delays or blocking calls.
    • Dependencies: Check if any dependencies or libraries used in your function are causing delays.
    1. Environment Differences
    • Local vs. Cloud Environment: The environment where your .exe runs can have a significant impact. The local machine typically has more dedicated resources compared to shared environments in the cloud.

    Recommendations:

    1. Increase Plan Resources: Try scaling up your Elastic Premium Plan to provide more CPU and memory resources.
    2. Optimize .exe Process: Profile and optimize your .exe process to ensure it runs as efficiently as possible.
    3. Consider Dedicated VM: If the function app continues to experience performance

    issues, consider running the .exe process on a dedicated VM or Azure Virtual Machine Scale Set where you have more control over the environment and resources.

    Detailed Steps for Investigation and Optimization

    Step 1: Scale Plan Resources

    1. Upgrade Plan: Go to the Azure portal, navigate to your Function App, and select the Scale out (App Service plan) under the Settings section.
    2. Select a Higher SKU: Choose a higher tier within the Elastic Premium Plan (e.g., from EP1 to EP3) to allocate more CPU and memory resources to your function.

    Step 2: Optimize the .exe Process

    1. Profiling: Use profiling tools to analyze the performance of your .exe process and identify any potential bottlenecks.
    2. Code Optimization: Optimize the code of your .exe to improve its execution time, such as minimizing I/O operations, optimizing algorithms, and ensuring efficient memory usage.

    Step 3: Monitor Performance

    1. Application Insights:
      • Set up Application Insights for your Azure Function App.
        • Use the Application Map, Performance, and Live Metrics features to monitor the execution time and resource usage of your function.
          • Look for any anomalies or patterns in the logs that could indicate specific causes for the delay.

    Step 4: Warm-Up Strategy

    1. Warm-Up Function:
      • Implement a warm-up function that runs periodically to keep your function app instances warm and reduce cold start latency.
        • You can create a simple HTTP trigger function that runs on a schedule using a Timer trigger.

    Step 5: Process Management

    1. Correct Management:
      • Ensure that the .exe process is being correctly spawned and managed within the function.
        • Use asynchronous patterns to avoid blocking the main thread of your function app.

    Step 6: Investigate Environment Differences

    1. Check Differences:
      • Compare the environment configurations between your local machine, Azure App Service, and the Function App.
        • Ensure that there are no significant differences in terms of software dependencies, configuration settings, and environment variables.

    Example of Profiling and Optimization

    Profiling the .exe Process

    • Use tools like Visual Studio Profiler, JetBrains dotTrace, or other profiling tools to analyze the CPU and memory usage of your .exe process.
    • Identify the time-consuming parts of the code and focus on optimizing those sections.

    Example: Implementing a Warm-Up Function

    1. Create a Warm-Up Timer Trigger Function:
         csharpCopy code
         using
      
      Deploy and Test:
      • Deploy the warm-up function to your Function App.
        • Monitor the execution times to see if the warm-up function helps in reducing cold start delays.

    By following these steps, you should be able to identify the cause of the slowdown and implement the necessary changes to improve the performance of your .exe process within the Azure Function App on an Elastic Premium Plan.There are several potential factors that might be causing the significant slowdown in execution time of your .exe process when running within an Azure Function App on an Elastic Premium Plan..

    0 comments No comments