Accurate prediction of application performance is vital, yet challenging in clustered environments. This paper addresses this challenge by discussing the use of stochastic values to parameterize cluster application performance models. Stochastic values, representing a range of likely behavior, are effective model parameters. The paper describes two representations for stochastic model parameters, demonstrating their effectiveness in predicting the behavior of applications under different workloads on a network of workstations. This study contributes to the development of more reliable and accurate models for predicting application performance in resource-contended environments, ultimately leading to improved resource management and application efficiency.
Published in the International Journal of Foundations of Computer Science, this paper aligns with the journal’s focus on theoretical foundations and mathematical aspects of computer science. By exploring the use of stochastic values in application performance models, it contributes to the journal’s scope on formal methods, algorithms, and computational models for understanding complex systems.