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I need help to find a way to generate stress/capacity testing on webpage.

Server Configuration of Load generator

  • Processor: AMD EPYC 7R13 @ 3.6 GHz, 4 Cores
  • Operating System: Ubuntu 22.04 (x86_64)
  • RAM: 16 GB
  • Hard Disk: 58 GB

Server Configuration of Target server Target Web Server (EC2)

  • Processor: AMD EPYC 7R13 @ 3.6 GHz, 2 Cores
  • Operating System: Ubuntu 22.04 (x86_64)
  • RAM: 8 GB
  • Hard Disk: 40 GB

Target Database Server (RDS)

  • Processor: AWS Graviton2, 2 Cores
  • Operating System: Custom RDS (x86_64)
  • RAM: 16 GB
  • Hard Disk: 60 GB

My script works well with 32k threads for 30 minutes, but more than that, it is not handling. please suggest me any solution.

I tried to find other tools which I can use for that.

I have limitations like:

    1. Go with the free resources.
    1. Not to go with distributed testing.

2 Answers 2

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What is your bottleneck?

  1. JMeter needs to be properly tuned for high loads, make sure to follow JMeter Best Practices and recommendations from 9 Easy Solutions for a JMeter Load Test “Out of Memory” Failure article.

  2. Your operating system needs to be tuned as well in particular:

  3. If you're still hitting CPU or RAM limits consider using i.e. Tsung which is known for producing higher throughput from the same hardware. But be aware that you won't be able to create more than 65535 because this is the maximum number of TCP ports

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Why not take an alternative path. You can find ~80% of all performance defects with a single user.

  • Turn on time-taken in your HTTP access logs

  • Use a profiler model to collect response times during unit, manual, and automated functional testing.

  • Find and address the highest resource use items

  • Where a page is running long, address the rendering or data storage (i.e. missing index) that is driving a page response time to slow.

If you have an existing version of the application in production, even better.

If you genuinely have 32000 consistent concurrent users on your host, the amount of risk of failure is so substantial that even the most expensive tools on the market pale in comparison to an outage risk. your demand of free is at odds with the risk and the value of the mitigated risk.

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