This post defines the exact meaning of software scalability testing, highlights its benefits, and discusses how to perform appropriate tests. It is the testing of a software application for measuring its capability to scale up in terms of any of its non-functional capability like load supported, the number of transactions, the data volume etc. 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. 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.
Scalability testing, is the testing of a software application to measure its capability to scale up or scale out in terms of any of its non-functional capability. Network usage is measured in terms of bytes received per second, frames received per second, segments received and sent per second, etc. If you have various requests for the same help that performs the same calculation, let each finish before starting. Reset everything before executing a test to ensure that the tests do not influence your current one. It is recommended to restart the entire software system, but you can leave your hardware running. This is time-consuming, but changing too much at once can make your application’s performance worse.
We are not concerned with short-term flexible provision of the resources . The purpose of elasticity is to match the service provision with actual amount of the needed resources at any point in time . Scalability is the ability of handling the changing needs of an application within the confines of the infrastructure by adding resources to meet application demands as required, in a given time interval . Therefore, the elasticity is scaling up or down at a specific time, and scalability is scaling up by adding resources in the context of a given time frame.
Why Do We Need To Perform The Scalability Testing?
Before performing the scalability testing, we must analyze how much memory would be sufficient for the virtual user and load test master. An application is deemed to be scalable if it can deliver the same throughput for varying levels of load on the internal applications, hardware, and database. Scalability tests check the ability of an application to scale up or scale down on any of its non-functionality requirements such as load, number of transactions, number of servers, volume of data etc. The complexity of parsing the XML/JSON payloads means that we have to put an extra focus on scalability testing, while the issue of security means we will have to focus some extra on doing testing of requests that are secure.
Similarly, Hwang et al. introduces a set of experiments involving five benchmarks, three clouds, and set of different workload generators. Only three benchmarks were considered for scalability measurements, the comparison was based on the scaling scenarios, and what the effect on performance and scalability. Gao et al. run the same experiments in two different AWS EC2 instance types, one with load-balancing https://globalcloudteam.com/ and one without. While Vasar et al. introduces a framework for testing web application scalability on the cloud, run the same experiments settings to measure response time on three different EC2 instance types. The first set of policies are the default policies that are provided by EC2 cloud when setting up an Auto-Scaling group . We pick out random scaling policies for the second set of experiments .
In order to try to improve the scalability of any software system, we need to understand the system’s components that effect and contribute to scalability performance of the service. This could help to design suitable test scenarios, and provides a basis for future opportunities aiming to maximize the services scalability performance. Assessing scalability from utility perspective is insufficient for the above purpose, as it works from an abstract perspective which is not necessarily closely related to the technical components and features of the system. You need to perform scalability tests if you want your applications to meet the requirements of your target customers. If you have not adopted scalability testing in your development process, you should.
The throughput feature is used to specify the amount of work implemented by the application in the given time. To find out the client-side degradation, end-user involvement under load, and server-side stability. However, horizontal scaling can be extremely expensive, not only the cost of entire systems such as servers, but we must also take into account their regular maintenance costs . When we have a proportional outcome, there are no bottlenecks as we scale up and increase the load the system is placed under. Before you actually create the tests, develop a detailed test plan. It is an important step to ensure that the test conforms as per the application requirement.
Types Of Testing
In contrast, Azure shows lower quality scalability than EC2 in this respect, with the metric being 0.45 in the first scenario, and 0.23 for the second scenario. We used the Redline13 Pro services to test Mediawiki, which allows us to test the targeted application by covering HTTP requests for all pages and links, including getting authentication to the application’s admin page. In this paper, we report the behavior of the service software in response to the most basic service request, i.e. a generic HTTP request. For our purposes it was sufficient to issue the simplest HTTP Request, i.e. logging in to the software service and getting in response an acceptance of the login request. Figure4 illustrates our way to test the scalability of cloud-based software services. Equation means that the volume of software instances providing the service scale up linearly with the service demand.
We also aim to consider further demand patterns to see the impact of these scenarios on the scalability performance of cloud-based software services. To achieve fair comparisons between two public clouds, we used similar software configurations, hardware settings, and a workload generator in the experiments. To measure the scalability for the proposed demand scenarios for the first cloud-based software service hosted in EC2 and Azure. The average number of OrangeHRM instances for both scenarios and for the four demand workloads are shown in Fig.6. The average response times for both scenarios and four demand workloads are shown in Fig.7.
Continuous testing plays a different role in the development process. It is used to check an application’s performance by increasing or decreasing the load in particular scales known as scalability testing. In the term of average response time, we note that there are big differences in the average of response times for the second scenario as it gradually from 2.035 s for demand size 100 to 9.24 s for demand size 800. While it graduates from 1.02 s for demand size 100 to 3.06 s for demand size 800, for the second scenario- Step-wise increase and decrease.
Create a thorough research schedule before you finally build the experiments. It is an essential step to ensure that the evaluation conforms to the specifications of the application. Breaking processes into stages and separating them into queues to be executed by a minimal no of workers can give you a performance boost. Store only necessary data that help to improve your business or application.
To achieve new heights, a company needs to improve the quality of the platform behind its success. Software scalability tests are imperative for any company operating in the digital market. Load Impact is a cloud-based testing system to create their websites, mobile applications, and APIs by performing a series of performance tests indicative of your systems’ stress endurance. However, for different applications, the definition of throughput may vary and is tested differently. The use of tools for scalability testing and a specified testing team for performance testing can lead to over budget. We need to analyze what type of CPU is required for the load test master and virtual users before executing the scalability testing.
Performance And Scalability Testing: Example All Native Knime Nodes
We calculated the scalability metrics ηI and ηt for the two demand scenarios for the cloud-based application for both cloud platforms. The calculated metrics for EC2 show that in terms of volume scalability the two scenarios are similar, the scaling being slightly better in the context of the step-wise increase and decrease of demand scenario. In contrast, Azure shows better volume scaling in the first scenario with around 0.65, while in the second scenario the volume scaling performance for the Azure is slightly less than the corresponding performance for the EC2. Our focus is whether the system can expand in terms of quantity when required by demand over a sustained period of service provision, according to a certain demand scenario.
While in term of quality scaling the the values has decresed 4.5% and 10% for the first and second scenarios respectively. If we draw a comparison between the two options of auto-scaling policies, we note that efficiency is increased Scalability vs Elasticity when we used the default auto-scaling policies . Companies use scalability tests to avoid losing money from technical problems. Through these tests, engineers will determine how to balance functionality and resources.
- The average number of MediaWiki instances for both scenarios are shown in Fig.9a, b.
- With a low load of 20 users, the average time is 5.5 seconds, as we increase the load to medium and a high load , the average time increases to 9.5 and 18 seconds respectively.
- The average number of OrangeHRM instances for both scenarios and for the four demand workload levels are shown in Fig.
- Network usage is measured in terms of bytes received per second, frames received per second, segments received and sent per second, etc.
- We must scale up in increments as each stage tests a different aspect.
- In terms of quality scalability, both systems scale much better in the context of the first scenario, steady rise and fall of demand, than in the case of the second scenario with step-wise increase and decrease of demand.
Tests need to focus on repeatable issues, although, some testing complications may be anomalous. Repeatable processes will help your engineers outline how an application runs overall. Testing the ability of a system, a network, or a process to continue to function well when it is changed in size or volume in order to meet a growing need.
To reduce the risk of losing money and its goodwill because of an application’s lousy performance, it is crucial to do Scalability testing before releasing it in a production environment. Correct measures should be taken in advance to rectify the problems and make the application more scalable. Consider implementing a CDN to help take some of your origin server’s load off and place it on the CDN’s servers for even faster performance.
From The Course: Performance Testing Foundations
You can create virtual users to test the platform’s load and functionality. After creating several test conditions, run your tests and check the results. In the testing phase, you can increase the number of requests performed. By increasing the number of requests, you can see when the application begins to respond slowly. As companies develop, they provide services at greater capacities.
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. In scalability testing, Memory usage is one of the resource utilizations used to sustain the memory expended for performing a task by an application. 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 scalability testing is downward scalability testing. When the load testing is not passed, we will use the downward scalability testing and then start decreasing the number of users in a particular interval until the goal is achieved.
Scalability Testing Results
You can assess an application’s throughput during the scalability testing phase. Throughput tests will show the number of processes an application can process at any given time. Software engineers use scalability testing to determine how your software will react and perform with different workloads. This style of testing outlines an app’s ability to scale up or down, which is essential when you need to know whether it will perform well in new environments. It prioritizes flexibility and agility throughout the testing stage.
What Kinds Of Performance Testing Are There?
Like memory usage, Central Processing Unit usage plays a role in the performance of your application. The CPU enables a PC to interact with all of its applications and programs. Some companies have a center of excellence that takes advantage of different testing processes. They can drive scalability and performance tests on your software. They operate around critical technologies and processes to improve efficiency.
In this paper, we demonstrate the use of two technical scalability metrics for cloud-based software services for the comparison of software services running on the same and also on different cloud platforms. The underlying principles of the metrics are conceptually very simple and they address both the volume and quality scaling performance and are defined using the differences between the real and ideal scaling carves. We used two demand scenarios, two cloud-based open source software services and two public cloud platforms .
This means that the current memory capacity can cope with all 3 stages of the test. It is a type of software testing that tests the ability of a system, a network or a process when the size/volume of the system is changed in order to meet a growing need. Test Strategy for Scalability Testing differ in terms of the type of application is being tested. If an application accesses a database, testing parameters will be testing the size of the database in relation to the number of users and so on. Programming techniques such as the use of compression techniques can help to reduce congestion and minimize network usage.
The scalability is an integral measurement of the behavior of the service over a period of time, while elasticity is the measurement of the instantaneous behavior of the service in response to changes in service demand. Furthermore, we are not concerned with the efficiency of the cloud-based software services delivery, which is usually measured by the consumption of resources (i.e. cost and power consumption) required to complete the desired workload . We calculated the integrated scalability metric for the two demand scenarios for all cloud-based applications for both cloud platforms. The values of the utility-oriented scalability metrics are shown in Table 8 – note that the MediaWiki experiments used more powerful and more expensive virtual machines than the experiments with the OrangeHRM on the EC2. Our utility oriented scalability calculations show that in the case of the systems that we compared the best choice is to use smaller and cheaper virtual machines on the EC2. We note that there are variations in average response times for the same cloud-based application hosted on two different cloud platforms .
Scalability Testing Attributes
They also check the criteria for the scalability test and decide the software tools needed to perform the test. The next feature of scalability testing is performance, which is used to check the user’s repetitively collective load and request under the webserver and repose of the system. Another type of performance testing is scalability testing, which comes under the non-functional testing of software testing. To find system instabilities that occur over time, you need to run tests for a long period. That is what soak testing is for; run load tests or even baseline tests over a long period of time and see how the target environment handles system resources and if it works properly.
Load Testing Basics
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. The scalability testing is needed to signify the user limit for the software product. There are advantages and disadvantages to both methods of scaling. Although scaling up may be simpler, the addition of hardware resources can result in diminishing returns. This means that every time we upgrade the processor for example, we do not always get the same level of benefits as the previous change. Vertical scaling, also known as scaling up, is the process of replacing a component with a device that is generally more powerful or improved.