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5 Best Practices for Cloud Performance Testing

Pratik Patel
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Pratik Patel
  • Apr 26, 2024
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    7 min read
5 Best Practices for Cloud Performance Testing
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The cloud has turned the environment of application development and deployment. Since the worldwide outbreak, some businesses have transferred to cloud testing services and technology, with an incredible 37% year over year rise after 2020. Cloud environments enable scalability, flexibility, and cost-effectiveness, making them perfect for modern applications.

Cloud-based testing helps in identifying challenges, measuring scalability, and ensuring that applications can manage expected and exceeded user loads. By proactively resolving performance concerns, you may avoid disruptions assure seamless operation, and ultimately offer the quality that consumers expect.

Different performance testing in cloud environment

The wonderful thing about cloud availability is that it supports a wide range of domains through various cloud environment types. Here are a few of them:

Infrastructure as a Service (IaaS)

Provides the most control over resources (virtual machines, storage, and networking) but requires the most configuration and management effort. Performance depends on the chosen resources and configuration.

Platform as a Service (PaaS)

Offers a development platform with pre-configured resources. Provides a balance between control and ease of use. Performance may be limited by the underlying infrastructure offered by the provider.

Software as a Service (SaaS)

Delivers complete applications over the Internet. Least control over resources but is the easiest to use. Performance depends entirely on the provider’s infrastructure.

Understanding Cloud Performance Testing

The practice of assessing an application's performance that has been deployed in a cloud environment is called cloud performance testing. It assists in measuring scalability, locating any bottlenecks, and confirming that the application satisfies performance goals under varied load scenarios.

Key cloud performance testing metrics

  • Response Time
    Time taken for the application to respond to a user request.
  • Throughput
    The number of requests handled in a certain amount of time.
  • Scalability
    Ability of the application to handle an increasing user load.
  • Resource Utilization
    How efficiently the application utilizes cloud resources (CPU, memory, and network).
  • Concurrency
    Ability of the application to handle multiple user requests simultaneously.

Types of cloud performance testing

Cloud-based testing encompasses various approaches, each targeting specific aspects of application behavior under load. A range of techniques and methodologies are tailored to assess different aspects of app performance in the cloud environment.

Load Testing

Cloud performance testing simulates increasing user loads to find performance bottlenecks. For an e-commerce site during a holiday sale, it progressively increases concurrent users to see if the system can handle the load without crashing. Key metrics are response time, throughput, and resource utilization. For more details, see our guide on Concepts and Metrics of performance testing.

Stress Testing

Stress testing, also known as load testing, aims to evaluate an application's performance under extreme conditions. By simulating a high volume of user logins and transactions, we can identify the application's breaking point, determine system limits, and ensure stability under peak loads. This helps in understanding how the system behaves under pressure and in planning for capacity and scaling.

Scalability Testing

Scalability testing evaluates an application's ability to scale resources to handle increased traffic without compromising performance. For example, a cloud-based video streaming service, it gradually ramps up user load to observe how well the system scales, such as by adding more servers. The objective is to ensure optimal performance under increased traffic. Key metrics include resource allocation, response time, and auto-scaling efficiency.

Soak Testing

Soak testing simulates sustained user load over extended periods to uncover stability issues. For example, in a social media platform, tests run for hours or days to detect memory leaks, database connection issues, and performance degradation. The objective is to identify long-term issues that may not be apparent in shorter tests. Key metrics include memory usage, response time consistency, and system stability.

Spike Testing

Spike testing simulates sudden bursts of traffic to assess an application's responsiveness. For example, a ticket booking website might test a surge in user requests when tickets for a popular event go on sale to see if the system can handle the spike without crashing. The objective is to evaluate the system's ability to manage sudden increases in load. Key metrics include response time during the spike, error rates, and system recovery time.

Cloud performance testing best practices

Now let’s get down to the main objective of this blog post, the five most effective practices or strategies that every quality assurance engineer must adopt for cloud performance testing.

Best practices for cloud performance testing

1. Setting clear performance objectives

Driving a car without a destination is like performance testing in cloud without clear objectives: you lack a roadmap to guide your efforts and measure progress. Clearly defined objectives are crucial—they ensure you gather meaningful data and know when you've achieved your testing goals.

The SMART framework helps establish focused and achievable performance goals. Here’s how to apply it:

  • Specific: Clearly define what to improve (e.g., reduce response time by 20%). Ex: Keep response times under 500 ms during peak hours and weekends, and maintain 99.9% uptime over a month.
  • Measurable: Track progress with metrics (e.g., average response time). Ex: Support a high number of concurrent users without exceeding 500 ms response time.
  • Achievable: Set realistic goals based on resources. Don’t aim for a 1-second response time if your application relies on complex calculations. Ex: Test with a dummy promotional event to handle the expected surge.
  • Relevant: Align business goals with user expectations to optimize UX. Ex: Prioritizing features that improve user experience (e.g., faster checkout) which in turn benefits the business (e.g., higher sales).
  • Time-bound: Set a timeframe to achieve goals. This creates a sense of urgency and helps prioritize testing efforts. Ex: Meet these targets before the holiday shopping season.

2. Perfect use of the cloud’s scalability

Cloud environments offer unmatched scalability through dynamic resource provisioning and de-provisioning in response to demand. This flexibility enables testers to replicate real-world scenarios with varying user activity and workload levels.

Unlike traditional on-premises infrastructure, cloud platforms provide instant scalability without the need for manual intervention. This makes it easier to conduct performance tests at scale, accommodating varying workloads and user activity levels seamlessly.

Types of performance scaling tests

  • Horizontal scaling tests: Add more application instances (horizontal scaling) to assess how the app behaves when distributed across multiple servers. This helps determine if your application can leverage horizontal scaling for increased capacity.
  • Vertical scaling tests: Increase resource allocation (CPU, memory) for a single application instance (vertical scaling) to measure performance gains with more powerful hardware. This helps identify if resource limitations are causing any bottlenecks.

Before and After cloud performance testing for an E-commerce platform.

The expected response time for the example project is 100 ms of response time.

Before and after cloud performace testing for an Ecommerce platform

3. Designing realistic test scenarios

Imagine testing a social media app with just login scenarios. It wouldn’t reflect real-world usage where users post content, interact with friends, and upload photos. Realistic test scenarios that mimic user behavior are crucial for uncovering potential performance issues that might arise during the actual use of the product.

Let’s look at how you could craft realistic scenarios:

  • User persona development: Create user personas representing different user types (e.g., casual browser, frequent poster, mobile user). Each persona should have a defined set of actions they perform within the application.
  • Think like a user: Map out typical user journeys, including login, browsing content, performing actions (e.g., posting a video, posting comments), and logout.
  • Load balancing: Simulate different user loads throughout the day. Morning login surges, peak traffic during business hours, and evening entertainment usage patterns should all be reflected in your test scenarios.

4. Always monitor and analyze performance metrics

Not monitoring and analyzing performance metrics is like conducting a science experiment without observing the results. Performance testing without real-time monitoring leaves you blind to the impact of your tests. Monitoring allows you to track key metrics and identify performance issues during testing.

Key KPIs to look out for during testing:

  • Response time: The amount of time it takes a program to reply to a request from a user. This is an essential user experience metric.
  • Throughput: The number of requests processed per unit time. This indicates how efficiently your application handles concurrent user activity.
  • Resource utilization: Track CPU, memory, and network usage to identify if resource limitations are causing performance issues.
  • Error rates: Monitor the number of errors encountered during testing. A spike in errors could indicate overloaded servers or application bugs.
  • Concurrency: Measure how well the application handles multiple user requests simultaneously. High concurrency issues can lead to slowdowns or crashes.

The majority of cloud service providers have integrated monitoring tools or third-party platform interfaces, such as Datadog and New Relic. These tools let you efficiently monitor performance indicators using real-time dashboards, visualizations, and alerts.

5. Optimize and Iterate

Test results are a gold mine for optimizing your cloud infrastructure. By analyzing the test results and resource limitations, you can:

  • Right-size resources: Adjust virtual machine configurations (CPU, memory) to ensure efficient resource usage without overpaying.
  • Auto-scaling: Implement automation testing that automatically scales resources (up or down) based on real-time demand. This helps maintain optimal performance while avoiding unnecessary costs.
  • Caching: Use caching techniques to save data that is accessed often, which will lighten the strain on the application server and speed up response times.

The importance of iterative testing

Performance optimization is an ongoing process, it's not a one-time thing, rather, there is always an opportunity to optimize the product to a level further beyond the threshold. Here’s why iterative testing is crucial:

  • Shifting business needs: As your business grows and user demands evolve, performance needs might change. Iterative testing helps adapt your cloud infrastructure to stay one step ahead of these changing requirements.
  • Evolving cloud landscape: Cloud providers constantly introduce new features and services. Iterative testing helps ensure your application takes advantage of these advancements for optimal performance.
  • Continuous improvement: Regular performance testing with evolving application features and usage patterns helps identify new bottlenecks and ensure ongoing performance excellence.

Top cloud performance testing tools

The best cloud-based testing tools for you will depend on your budget and unique requirements. Here is a summary of several well-liked choices, with an emphasis on their features, advantages, and special features.

PFLB

PFLB (Performance-Focused Load Balancer) is a cloud-native load balancing solution designed to optimize performance by distributing traffic across multiple instances. It uses real-time performance metrics to ensure efficient resource use and minimal latency for users.

  • Pros of PFLB:
    • Ability to seamlessly scale with increasing traffic volume, accommodating growing application demands without manual intervention.
    • The fault-tolerant architecture of PFLB ensures high availability and reliability, minimizing service disruptions.
    • PFLB offers intuitive configuration options, allowing users to define routing rules and load-balancing algorithms effortlessly.

PFLB’s unique selling point lies in its ability to prioritize performance optimization while maintaining fault tolerance and scalability. By dynamically adjusting traffic routing based on real-time performance data.

SOASTA CloudTest

SOASTA CloudTest is an adequate performance cloud testing platform that enables organizations to simulate real-world user behavior, analyze app performance, and identify bottlenecks. It offers a range of features, including load testing, stress testing, and real-user monitoring.

  • Pros of SOASTA CloudTest:
    • An easy-to-use interface offered by SOASTA CloudTest makes it easier to create, run, and analyze tests.
    • SOASTA Cloud Test provides a unified platform for all your performance testing needs, eliminating the need for multiple tools.
    • The platform offers visual scripting capabilities alongside traditional code-based testing, making it accessible to testers with varying technical skill sets.

CloudTest’s selling point lies in its ability to offer end-to-end performance testing capabilities, from test creation to result analysis. By providing a solution for load testing and monitoring.

K6

Using K6, an open-source performance testing tool, we can create JavaScript test scripts and run large-scale tests. Because of its developer-friendly architecture, teams can easily integrate cloud software testing into their pipelines for continuous integration and delivery.

  • Pros of K6:
    • K6 is lightweight and efficient, making it suitable for testing applications of any size or complexity.
    • Leverages JavaScript (ES6) for writing test scripts, offering a familiar and efficient language for developers.
    • K6 integrates seamlessly with cloud environments and containerization technologies like Docker.

K6's selling point lies in its simplicity and flexibility. By leveraging the power of JavaScript for test scripting, K6 enables us to create and execute performance tests with ease, facilitating rapid feedback loops and continuous improvement.

Gatling

A Scala-written high-throughput load testing tool for assessing application performance and simulating concurrent users is called Gatling. It is appropriate for testing complex web applications and APIs since it has a strong scripting engine and extensive reporting features.

  • Pros of Gatling:
    • Being open-source, Gatling offers greater flexibility and customization compared to some commercial tools.
    • Gatling can handle large-scale load testing scenarios effectively.
    • Gatling provides a wide range of features, including performance reports, comprehensive data analysis, and integration with CI/CD pipelines.

Gatling is a powerful option for experienced testers or development teams comfortable with Scala. It provides extensive features, scalability, and the flexibility of an open-source solution.

Conclusion

Cloud performance QA plays a pivotal role in this endeavor by enabling organizations to proactively identify and address performance issues, optimize resource utilization, and ensure the reliability and scalability of cloud-based applications.

As businesses increasingly rely on cloud-based solutions, ensuring exceptional cloud application performance has become a fundamental for success. That’s where Alphabin comes in. We are a trusted partner for businesses seeking to optimize their cloud applications. Our team of performance quality professionals will provide you an upper hand when it comes to performance quality.

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Frequently Asked Questions

How often should cloud performance testing be conducted?
FAQ ArrowFAQ Minus Arrow

Regular testing is essential. Conduct performance tests during development, before production deployment, and after any significant changes. Additionally, consider periodic testing to account for evolving workloads and infrastructure updates. You can contact us anytime if you need any expert advice. Contact us and explain your project details and queries; we’ll be more than happy to attend to you.

What are the key components of effective cloud performance testing?
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Various key components in cloud for performance testing are:

  • Workload Modeling: Workload modeling is the foundation of performance testing. It involves creating realistic scenarios that mimic user behavior and system load. By accurately simulating various user actions (such as login, browsing, transactions, etc.), we can understand how the application behaves under different conditions.
  • Scalability Testing: Scalability testing assesses how well an application can handle increased demand. It ensures that the system can gracefully scale up or down based on load fluctuations.
  • Latency Testing: Latency directly impacts the user experience. It measures the time taken for a request to travel from the client to the server and back.
  • Resource Monitoring: Tracking CPU, memory, and network usage during testing.
  • Failover Testing: Failover testing ensures that the application seamlessly transitions between cloud instances or servers during failures or maintenance.
What are the key metrics to monitor during cloud performance testing?
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Some key metrics to monitor during cloud performance testing include response time, throughput, error rates, CPU and memory utilization, and network latency. These metrics provide insights into application performance, resource utilization, and scalability, helping businesses identify areas for improvement and optimization.

What is resource monitoring during performance testing, and what are the key metrics for that?
FAQ ArrowFAQ Minus Arrow

Monitoring resource utilization during testing provides insights into system health and potential bottlenecks.

The key metrics to look out for during monitoring are:

  • CPU Usage: Identify CPU-bound scenarios.
  • Memory Usage: Detects memory leaks or excessive memory consumption.
  • Network Throughput: Measure data transfer rates.
  • Disk I/O: Evaluate read/write operations.

About the author

Pratik Patel

Pratik Patel

Pratik Patel is the founder and CEO of Alphabin, an AI-powered Software Testing company.

He has over 10 years of experience in building automation testing teams and leading complex projects, and has worked with startups and Fortune 500 companies to improve QA processes.

At Alphabin, Pratik leads a team that uses AI to revolutionize testing in various industries, including Healthcare, PropTech, E-commerce, Fintech, and Blockchain.

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