Techniques for Assigning Weights in Markov Chains for Web Application Load Testing

Techniques for Assigning Weights in Markov Chains for Web Application Load Testing

3 July 2024 Stephan Petzl Leave a comment QA

When conducting load tests for web applications, understanding user navigation patterns is crucial. Access logs typically provide relative hit rates for various URLs, but they often lack detailed information about user transitions between pages. This can be challenging when you need to determine the probability of users moving from one page to another.

To model variability in user behavior, one effective approach is to construct a Markov chain. Here, we will explore practical techniques for assigning weights to the edges of the Markov chain, ensuring that the sum of the weights of all edges out of a node equals 1.

Using Markov Chain Monte Carlo (MCMC) Methods

One promising technique is the Markov Chain Monte Carlo (MCMC) approach. MCMC methods are widely used for simulation calculations and can be particularly effective in this context. The basic idea is to start with a given long-run distribution and use Monte Carlo simulations to develop the actual Markov chain.

In practice, this involves iteratively sampling from the probability distribution to generate a sequence of states that approximate the desired distribution. Despite its complexity, MCMC is a powerful tool that can yield realistic models of user behavior.

Applying the PageRank Algorithm

Another approach is to use the PageRank algorithm. This algorithm is well-known for its application in search engines, where it ranks web pages based on their link structure. Here’s how you can apply it to your load testing scenario:

  • Construct your page links and initially assign equal weights to each possible linked page from the current page.
  • Introduce a small probability that the user will visit any page uniformly at random, simulating a user “surfing” to a random page.

This method ensures that pages with more links are visited more often, while still allowing for the possibility that any page can be visited. Although you already have specific hit rates, the PageRank algorithm can help generate realistic general data for these rates.


Both the MCMC approach and the PageRank algorithm offer robust techniques for assigning weights in Markov chains, aiding in the development of realistic models for user behavior in web applications. By leveraging these methods, you can enhance the accuracy of your load tests and better predict user navigation patterns.

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