Big data analytics have become widespread as a means to extract knowledge from large datasets. Yet, the heterogeneity and irregular- ity usually associated with big data applications often overwhelm the existing software and hardware infrastructures. In such con- text, the exibility and elasticity provided by the cloud computing paradigm o er a natural approach to cost-e ectively adapting the allocated resources to the application’s current needs. However, these same characteristics impose extra challenges to predicting the performance of cloud-based big data applications, a key step to proper management and planning. This paper explores three modeling approaches for performance prediction of cloud-based big data applications. We evaluate two queuing-based analytical models and a novel fast ad hoc simulator in various scenarios based on di erent applications and infrastructure setups. The three ap- proaches are compared in terms of prediction accuracy, nding that our best approaches can predict average application execution times with 26% relative error in the very worst case and about 7% on average
