6 Benefits Of Testing Cloud Applications With Jmeter


Despite its out-of-the-box features, JMeter is also open-source software that has entirely been designed on the Java application for stressing test websites and other application architectures. JMeter helps stimulate multiple users with concurrent threads, creating a heavy load that is against the web application under test.

For example, an M4 large example is sufficient for serving 250 requests per second. Nevertheless, additional users’ stimulation would require the launching of more than one JMeter instance by setting up the clusters along with multiple machines. However, the pay-as-you-go concept is not only an on-site operation but in a scenario where everyone migrates to the cloud with the handling of the technology paradigm change.

In turn, it has become a limitation of the fact that JMeter gives support to countless testing strategies like load testing, distributed testing, and functional testing. JMeter also uses Java RMI (Remote Method Invocation) for communicating with its slaves. Still, all these connections require all the servers on the same subnet, which is not a feasible option while using EC2 instances.

However, before we dive into the step-by-step instructions, let us all take a glance at the definition of some important terms.

  • Master – It is the system running JMeter GUI, which controls the test.
  • Slave – It is the system running JMeter server which takes commands from the GUI and  sends a request to the target system(s).
  • Target – It is the webserver which we plan to stress test.

Going further, there is one more master example that sends the test to 2 slaves. These two slaves execute the test and send the results back to the master to collect and combine the results.

Note

The test will be executed on both slim machines and will not be divided across them. It indicates that if one wants to run 300 in the test, the target would be hit with 600 threads. The master doesn’t execute any of the tests. Gathering results and orchestrating the test is enough for handling one machine.

The test will be then sent out to the slaves from the master. There is no requirement for copying the text file to all slaves.

Running JMeter on remote

The following points should be considered before starting with remote testing cloud applications with Jmeter:

  1. The fire was on and the systems were turned off.
  2. All clients are on the same subset.
  3. Making sure that JMeter can access the server.
  4. Turning on sharing from my computer.
  5. Turning on the sharing settings from the control panel and then going to advanced sharing settings.
  6. We use the same version of JMeter to run all the systems as the mixing version may not work correctly.
  7. We have changed the option to computer sleeves from the control panel, hardware, and sound, power options, and edit plan settings for all machines to never.

Confirming the channel’s communication and trying to ping from slaves to master and master to slaves once. Until remote testing, the following configurations must also be considered:

  1. Go to the bin folder of JMeter.
  2. Open the file in edit mode, “JMeter.properties.
  3. Go to segment “#Remote hosts and RMI setup.”

Mention the Slaves IP address/es as a “remote hosts” value, and make sure you’ve never commented on it.

Also, read 10 Ways to Speed Up Windows 10

Testing Cloud Applications with JMeter

Cloud-based performance testing

Cloud-based output monitoring lets us test our application on various channels with no bank interruption. Research teams may deploy testing scripts on the previously identified cloud load generators to minimize costs and efforts. Testing cloud applications with Jmeter also helps in stimulating load checks with multiple rivals from various geographies. Cloud output monitoring is distinct from non-cloud testing, and it is important to ensure an accurate approach.

Benefits of Cloud performance testing

Both evaluation levels can be carried out in cloud computing, but cloud environments can greatly benefit from efficiency checks.

Flexibility

In the comfort of an organization, different stages of monitoring may be carried out in distinct environments. Performance testers no longer have to wait for the end of the training process for efficiency and stress checks in a productive environment. Such an environment should instead be introduced at will.

Simplicity

The cloud model allows the opportunity to start as soon as the setup can be defined to provide a new standard of streamlined bug fixing systems.

Comprehensive testing

End-to-end cloud monitoring should be carried out with more common processes. Both components required for building the full device chain may be released in the cloud. Thus, it is important to test the whole company process.

Cost reduction

Cloud environments could be allowed and disabled at will, minimizing environmental maintenance costs. The biggest element in businesses’ preference for cloud is cost savings. According to IDC survey results, the main drivers of cloud adoption are economic benefits.

Cloud testing optimizes cloud technology, minimizes unit cost, and increases performance testing quality. The Cloud Research Vendor Study indicates that cost efficiency typically ranges between 40 and 70%.

Check out the insights of Functional Testing: The Complete Guide For Beginners.

Cleaner and greener testing

It is accurate that cloud computing power makes it considerably greener than conventional ones. Businesses can only access IT services on demand and eliminate waste by distributing cloud resources with their test infrastructure. Cloud-based users can minimize their energy utilization and save over 55 per cent in the atmosphere of carbon dioxide.

Greater control

Cloud-based environments could enhance test execution management, monitor application efficiency, and detect bottlenecks during tests. The cloud model helps research engineers to determine points of interference from a few thousand to millions of concurrent users.

Conclusion

The benefits which we receive from testing cloud applications with Jmeter are incomparable. Still, some concerns are associated with the cloud performance testing, which should be kept in mind, like the test results might not always be accurate because of the varying performance caused by the provider’s network condition.

Sometimes, there can also be chances of a service outage from the provider, and we might not always be getting the same resources. Also, there would be some challenges associated with the migration or the move from the traditional method to the cloud.