Load testing throughout the development cycle has become an essential part of the process of designing scalable, reliable Web applications. Developers and QA professionals now rely on load testing tools as a means to validate system architectures, tune applications for maximum performance, and assess the impact of hardware upgrades. Consequently, it is critical that the load test results can be used with confidence as the basis for key decisions about application readiness and potential changes to the system’s hardware and software. Using the methodology embodied in this guide along with accurate load testing tools such as Oracle Load Testing for Web Applications, you now have a systematic approach to ensure the performance of your Web applications.

scalability testing

We have 8 instances of nova-scheduler because it’s known as non-scalable inside of service (there is no workers/threads/etc inside of nova-scheduler). Each and every “Compute node” container has neutron-ovs-agent, libvirtd, nova-compute and openvswitch-vswitch inside. Yellowfin demonstrated that when deployed appropriately, a level of scalability that can meet the needs of extranet reporting deployments that span thousands of users. Scalability refers to the ability of a system to perform well in the face of increasing user demands. For example, a system with a high degree of scalability will behave predictably when the number of users increases from 100 to 1000 to 10,000 and more.


During load testing, you can monitor response times, throughput, server conditions, and more. The goal of load testing is not to break the target environment though. If the above two scenarios execute without any problems, the next step is to execute the full load test with the full number of difference between scalability and elasticity virtual users of each user-group type. Once the individual Visual Scripts have been created and verified, you can create and verify the load test scenarios. It will save you a lot of time and aggravation if you perform a number of simple verification steps before your full-blown load test.

scalability testing

The purpose of scalability testing is to check whether your system scales appropriately to the changing load. For example, a larger number of incoming requests should cause proportial increase in the response time. To find system instabilities that occur over time, you need to run tests for a long period.

It is recommended to perform clustering on the application and database server layers for high concurrency rates, and is the best approach with high availability and failover requirements in mind. Results of this testing process show that Yellowfin can scale to support large user installations. Usually, it includes a set of load tests with different hardware and software settings, and keeping the test environment unchanged.

Performing load tests with a smaller number of concurrent users and extrapolating the result for larger numbers. A system’s scalability is thus important when considering existing and projected needs. Scalability should be a key criterion when determining the hardware and software environment in which to run your BI solution. If we want to perform the scalability testing, we need to verify what operating systems are prepared by the load generation managers and load test master. Another type of performance testing is scalability testing, which comes under the non-functional testing of software testing.

Scalability Testing Operations

Allow for easy maintenance of the Visual Scripts as the application changes so that scalability can be re-verified each time that the system is changed. Yellowfin provides high throughput for scheduled reporting environments. The Yellowfin test results presented in this section are a result of the modern, open, and scalable architecture that was built to provide true enterprise level reporting for global organizations. Sometimes, the test environment is not always precisely similar to a production environment.

After that, we will set the testing environment, configure the hardware required to implement a scalability test, generate, validate the visual script and the load test scenarios. The hardware and software components used in the testing were designed to simulate an enterprise-reporting environment in which user activities include logging in, running dashboards and reports, and logging off. Before performing the scalability testing, we must analyze how much memory would be sufficient for the virtual user and load test master. It is used to check an application’s performance by increasing or decreasing the load in particular scales known as scalability testing. Once you have created and verified the basic load test scenarios above, you can begin to run the load test scenarios with many virtual users, and expect that the test results will be valid.

  • To perform scalability testing, requires a high-level of testing knowledge and skills.
  • The scalability testing will help us to save a lot of money and time if we identify the cause of multiple performance issues in the specific application during the testing phase.
  • In other words, we can say that the performance of the application depends on its type as it is tested continuously for several users, which can support without its failure or backup condition.
  • For example, in a database application, it is sustained in several commands managed in a unit of time.

Normally, the CPU usage is calculated in respect of the unit Megahertz. The throughput feature is used to specify the amount of work implemented by the application in the given time.

Planning The Scalability Tests

The user limit is the maximum number of concurrent users that the system can support while remaining stable and providing reasonable response time to users as they perform a variety of typical business transactions. To insure that you have the appropriate hardware to execute scalability tests, ask your load testing tool vendor to provide you with the hardware requirements for the load test master and agent machines. Named users make up the total population of individuals who can be identified by and potentially use the system. They represent the total user community, and can be active or concurrent at any time. In a real-life BI environment, this is the total number of individuals authorized to use the system. It is the number of most interest when planning a BI implementation, because it tells you how many users you can expect to support in a given environment with the response times reported in a test environment.

Ideally this is run on a newly deployed cloud each time for repeatability purposes. Cloud deployment should be documented for each test case / run as deployment will set many configuration values which will impact performance. Here we would like to just point out some findings about resources consumptions by each and every service which could help with servers capacity planning. Here is top watermarks from different services under mentioned test load.

After the enhancement, the software is working correctly and efficiently in meeting the user requirements and expectations. To find out the client-side degradation, end-user involvement under load, and server-side stability. It is specified as the capacity of a network, system, application, product, or process to make the function correctly when modifications are made in the system’s size or volume to meet an increasing need. In early spring 2018 Kubernetes went into Production at Meltwater and has seen great adoption from a lot of our engineering teams. Our Kubernetes cluster went from hosting 150 services at the end of 2018 to more than 800 services 9 months later. For CSM Web applications, all tests were conducted against the demo.czar file in an as-is condition with no custom Business Objects or operations.

Provide reports and a performance database to allow for post-run analysis and comparison with previously established benchmarks. In conclusion, the results of the Yellowfin benchmark tests indicate that when deployed appropriately, Yellowfin is a high performance solution that can be the basis for enterprise reporting initiatives. To judge any system’s scalability, a realistic evaluation of system performance in a carefully defined and controlled test situation is needed as a benchmark, or guideline, to use when configuring server environments. The testing described in this section was designed to develop reliable benchmarks for Yellowfin. As the number of individuals using a reporting system grows, and the number of queries and reports generated increases, performance can become an issue.

In contrast, scalability testing of a web server depends on the number of requests processed. In this section, we are going to understand scalability testing, which checks the performance of an application by increasing or decreasing the load in particular scales like number of a user. The user limit should be higher than the required number of concurrent users that the application must support when it is deployed. It is important to note that in a real-world environment, other factors such as the network and database layers could be potential key performance inhibitors.

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Later we modified original libvirt driver to only skip actual VM booting (spawn of qemu-kvm process). Any other error reports that may be required for the development group to debug and fix any problems that may have occurred. After planning the Visual Scripts, you will use Oracle Functional Testing for Web Applications to create and verify each Visual Script. Customers are better able to extrapolate from the results based on the level of concurrency in their reporting environment. Test results reflect high usage patterns with user behavior mimicked for active concurrency testing. To ensure that the test users were truly concurrent, meaning that they were simultaneously stressing the server.

Software Testing Tools

It enhances the assurance in a software product to see the future development difficulties. Additionally, the test environment requests to be persistent and fixed for every diverse enhanced load. Typically, the memory usage is calculated in respect of the unit bytes.

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Occasionally, tests are working fine but fail in the testing phase because of the incorrect test scripts and test scenarios, which waste a lot of time doing pointless modifications. In other words, we can say that the performance of the application depends on its type as it is tested continuously for several users, which can support without its failure or backup condition. Therefore, we can quickly identify the bottleneck by performing downward scalability testing. The scalability testing is needed to signify the user limit for the software product. Performance testing is one of the most common tasks in web service testing and probably one of the areas where the confusion is most common. We will try to give a short overview to performance testing here and what it means for web service testing.

Record versions of Linux kernel, Base Operating System (ex. Centos 7.3), OpenStack version (ex. Newton), OpenStack Packages, testing harness/framework and any other pertinent software. Oracle Functional Testing for Web Applications Data Bank Wizard lets you specify and connect external data sources to the Visual Scripts. For Scalability Testing, the Test Strategy is different regarding the type of application being tested. It is used to evaluate the application to stop scaling and identify the reason behind it. The network usage is calculated in terms of frames received per second, and bytes received per second, segments received and sent per second, etc.

Performance refers to the amount of time it takes to complete a particular task. Usually, the response time of an application reduces as the user load is enhanced. In other words, we can say that the response time checks how fast the system or the application response to user or other application requests. For example, in a database application, it is sustained in several commands managed in a unit of time. In contrast, it is uniform in the number of user requests handled in a unit time in a web application. For the scalability testing, the test strategy can be changed based on the type of application being tested.

Baseline testing examines how a system performs under expected or normal load and creates a baseline with which other types of tests can be compared. Prime aim of this simple testing is to check scalability of OpenStack control and data plane services. Because of that RabbitMQ and MySQL were run in single node mode just to verify “essential load” and confirm that https://globalcloudteam.com/ there is no issues with standalone nodes. In addition, you will also need to gather data from other tools that monitor the state of different components in the application architecture. This will allow you to correlate the client-side degradation noticed during the scalability tests to one or more scalability problems with specific components of the application.

Oracle Load Testing for Web Applications Autopilot lets you run multiple scenarios with different virtual user characteristics. Determine and configure the hardware and environment needed to execute the scalability tests. A hardware configuration document is usually available from each test vendor that explains the hardware requirements for a particular load testing setup. In addition to Oracle Application Testing Suite, you may need additional software tools for other specialized monitoring or reporting. If we use the advanced tools for Scalability testing and an exclusive testing team for performance testing, it will cause an over budget for the projects.

What To Avoid When Testing For Scalability

Boot 50 persisting instances every 1200 seconds until 1000 instances booted and running in OpenStack cloud. The environment description includes hardware specs, software versions, tunings and configuration of the OpenStack Cloud under test. In folder with this report you’ll find additional files with the test scenario, results and usage patterns observations. We have used Mirantis MOS 8.0 official repo for creating containers with OpenStack services.

Once the test development has been performed, we will analyze the recorded results and build the necessary report. Our Foundation team is providing Kubernetes as a service for the rest of our engineering teams. They wanted to know how much more Kubernetes could grow when onboarding the remaining teams with the current setup and architecture. Some One-Steps were created to simulate record create, update, and delete operations. If the requests and responses are either complex or large, or if they include large attachments, we have to focus on emphasizing that complexity and see how it behaves under load.

Scalability Testing Content And Operations

And we also learn about its needs, the purpose of scalability testing, type of scalability testing, prerequisite for scalability testing, various features of scalability testing, how to perform it, advantages and disadvantages. As part of our involvement in the Cloud Native Nordics community, we recently gave a tech talk there where the team managing our Kubernetes cluster shared what they learned about scalability testing Kubernetes. This means it does not necessary have to be how a system performs under high load, it can also be how it performs under base load or expected load. It doesn’t even have to be structured, automated or created in a tool like SoapUI; simply refreshing your web browser over and over again very fast is a load test. Review System performance metrics graphs during test duration to observe for stopping/failure conditions.

If we execute the scalability testing, we can control the end-user experience under the specific load. Hence, the helpful procedures can be taken earlier to fix the issues and make the application more accessible. Another resource utilization under scalability testing is CPU usages, which is used to calculated the CPU utilization while performing application code commands.