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Performance Modeling Process in IT Systems

 Performance is a critical factor in software systems. Often, performance issues in applications are identified too late—either during performance testing or, worse, in production by end users. At that stage, fixing performance problems becomes costly due to the extensive architectural, design, and code changes required, along with the associated rework and testing.

To avoid such late-stage performance issues, it is essential to design systems with performance in mind. This involves:

  • Defining performance objectives
  • Designing the system to meet those objectives
  • Evaluating system performance under expected load conditions before making significant investments

Performance modeling plays a crucial role in predicting how an application will behave under load. It involves simulation techniques and data analysis—either based on historical records or real-time data collection—to estimate system performance under specific conditions.

Performance Modeling Process

1. Identify Key Scenarios

Determine which scenarios are critical to performance and pose the highest risk to meeting performance objectives.

2. Identify Workloads

Define the expected number of users and concurrent users the system must support.

3. Identify Performance Objectives

Establish performance goals for each key scenario. These objectives should align with business requirements.

4. Identify Constraints (Budget)

Define resource constraints such as:

  • Maximum response time
  • CPU, memory, disk I/O, and network I/O limits

5. Identify Processing Steps

Break down each scenario into its component processing steps.

6. Allocate Budget

Distribute the available resource budget (from Step 4) across the processing steps (from Step 5) to meet the performance objectives.

7. Evaluate Design

Assess the system design against performance objectives and constraints. Adjust the design or reallocate the response time and resource utilization budget as needed.

8. Validate the Model

Continuously validate performance models through prototyping, testing, and measurement.

Types of Performance Modeling

Not all performance models are suitable for every application. Choosing the right model is key—many failures in performance modeling result from selecting an unsuitable model rather than flaws in the modeling approach itself.

1. Integrated Performance Model

In this approach, performance modeling is integrated directly into the software development process. This method provides precise performance insights but requires significant effort and control over the software development lifecycle.

Applicability:

  • When performance is a top priority for the business or users
  • When the application is under direct development control

Pros:

  • Provides accurate and detailed performance insights

Cons:

  • Requires significant effort, time, and investment

2. Component-Based Performance Model

This approach is suitable when tightly integrated performance modeling is not feasible. Instead, performance is analyzed at the component level, making it more practical for certain use cases.

Applicability:

  • Packaged software solutions
  • Applications divided into independent components
  • When cost and effort constraints exist

Pros:

  • Easier to implement
  • Requires less effort compared to an integrated model

Cons:

  • Less precise in providing detailed performance insights for developers and architects

Performance Modeling Approaches

  1. Discrete Event Modeling – Used in integrated or tightly coupled performance modeling.
  2. Analytical Modeling – Uses simulation-based techniques to predict component behavior under specific conditions.
  3. Statistical Modeling – Uses statistical analysis to model application performance based on observed results under varying load conditions.

By applying the right performance modeling process and approach, IT teams can proactively address performance concerns early in the development lifecycle, reducing risks and optimizing system performance efficiently.


Interesting resources

1. http://jmt.sourceforge.net/

2. https://www.palladio-simulator.com/tools/

3. http://www.perfdynamics.com/Tools/PDQ.html

4. https://www.dynatrace.com/news/blog/how-to-create-performance-models-using-application-monitoring-data/

5. https://www.vtt.fi/inf/pdf/publications/2003/P512.pdf

6. https://waset.org/publications/3902/performance-modeling-for-web-based-j2ee-and-.net-applications

7. https://pdfs.semanticscholar.org/d688/37cbfb63c5c3469a7db7c520881001a039b8.pdf

8. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.10.2690&rep=rep1&type=pdf

9. http://alicezheng.org/papers/sigmetrics10-practicalperf.pdf

10. http://www.archer.ac.uk/training/course-material/2018/07/ScaleMPI-MK/Slides/PerformanceModelling.pdf

11. https://hal.archives-ouvertes.fr/hal-01118352/document

12. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.3046&rep=rep1&type=pdf

13. http://www.scs-europe.net/dlib/2013/ecms13papers/hipmos_ECMS2013_0178.pdf

14. http://www.iaeng.org/publication/WCECS2010/WCECS2010_pp35-43.pdf

15. http://jmt.sourceforge.net/Papers/acm09jmt.pdf

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