Cloud computing provides a cheap and elastic platform for executing large scientific workflow applications, but it rises two challenges in prediction of makespan (total execution time): performance instability of Cloud instances and variant scheduling of dynamic schedulers. Estimating the makespan is necessary for IT managers in order to calculate the cost of execution, for which they can use Cloud simulators. However, the ideal simulated environment produces the same output for the same workflow schedule and input parameters and thus can not reproduce the Cloud variant behavior. In this paper, we define a model and a methodology to add a noise to the simulation in order to equalise its behavior with the Clouds' one. We propose several metrics to model a Cloud fluctuating behavior and then by injecting them within the simulator, it starts to behave as close as the real Cloud. Instead of using a normal distribution naively by using mean value and standard deviation of workflow tasks' runtime, we inject two noises in the tasks' runtime: noisiness of tasks within a workflow (defined as average runtime deviation) and noisiness provoked by the environment over the whole workflow (defined as average environmental deviation). In order to measure the quality of simulation by quantifying the relative difference between the simulated and measured values, we introduce the parameter inaccuracy. A series of experiments with different workflows and Cloud resources were conducted in order to evaluate our model and methodology. The results show that the inaccuracy of the makespan's mean value was reduced up to 59 times compared to naively using the normal distribution. Additionally, we analyse the impact of particular workflow and Cloud parameters, which shows that the Cloud performance instability is simulated more correctly for small instance type (inaccuracy of up to 11.5%), instead of medium (inaccuracy of up to 35%), regardless of the workflow. Since our approach requires collecting data by executing the workflow in the Cloud in order to learn its behavior, we conduct a comprehensive sensitivity analysis. We determine the minimum amount of data that needs to be collected or minimum number of test cases that needs to be repeated for each experiment in order to get less than 12% inaccuracy for our noising parameter. Additionally, in order to reduce the number of experiments and determine the dependency of our model against Cloud resource and workflow parameters, the conducted comprehensive sensitivity analysis shows that the correctness of our model is independent of workflow parallel section size. With our sensitivity anal- ysis, we show that we can reduce the inaccuracy of the naive approach with only 40% of total number of executions per experiment in the learning phase. In our case, 20 executions per experiment instead of 50, and only half of all experiments, which means down to 20%, i.e. 120 test cases instead of 600. (C) 2017 Elsevier B.V. All rights reserved.