This paper presents an approach and a corresponding tool for generating probabilistic and intensity-varying workload for Web-based software systems. The workload to be generated is specified in two types of models. An application model specifies the possible interactions with the Web-based software system, as well as all required low-level protocol details by means of a hierarchical finite state machine. Based on the application model, the probabilistic usage is specified in corresponding user behavior models by means of Markov chains. Our tool Markov4JMeter implements our approach to probabilistic workload generation by extending the popular workload generation tool JMeter. A case study demonstrates how probabilistic workload for a sample Web application can be modeled and executed using Markov4JMeter.