Performance Assurance Model for Applications on SPARK Platform

The wide availability of open source big data processing frameworks, such as Spark, has increased migration of existing applications and deployment of new applications to these cost-effective platforms. One of the challenges is assuring performance of an application with increase in data size in production system. We have addressed this problem in our work for Spark platform using a performance prediction model in development environment. We have proposed a grey box approach to estimate an application execution time on Spark cluster for higher data size using measurements on low volume data in a small size cluster. The proposed model may also be used iteratively to estimate the competent cluster size for desired application performance in production environment. We have discussed both machine learning and analytic based techniques to build the model. The model is also flexible to different configurations of Spark cluster. This flexibility enables the use of the prediction model with optimization techniques to get tuned value of Spark parameters for optimal performance of deployed application on Spark cluster. Our key innovations in building Spark performance prediction model are support for different configurations of Spark platform, and simulator to estimate Spark stage execution time which includes task execution variability due to HDFS, data skew and cluster nodes heterogeneity. We have shown that our proposed approaches are able to predict within 20% error bound for Wordcount, Terasort, K-means and few TPC-H SQL workloads.