Formal Performance Evaluation of the Map/Reduce Framework Within Cloud Computing

The recent appearance, evolution and massive expansion of social media-based technologies, in conjunction with what currently is known as Internet of Things, results in a vertiginous data production. One of the main contributions to address this matter has been the Hadoop framework (which implements the Map/Reduce paradigm), especially when used in conjunction with Cloud computing environments. In this paper, a comprehensive and rigourous study of the Map/Reduce framework using formal methods is presented. Specifically, the Timed Process Algebra BTC is used, and the resulting formal model is evaluated with a real social media data Hadoop-based application. Moreover, the formal model is validated by carrying out several experiments on a real private Cloud environment. Finally, the formal model outcomes are harnessed to determine the best performance–cost agreement in a real scenario. Results show that the proposed model enables to determine in advance both the performance of a Hadoop-based application within Cloud environments and the best performance–cost agreement.