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Distributed data processing frameworks (e.g., Hadoop, Spark, and Flink) arewidely used to distribute data among computing nodes of a cloud. Recently,there have been increasing efforts aimed at evaluating the performance ofdistributed data processing frameworks hosted in private and public clouds.However, there is a paucity of research on evaluating the performance of theseframeworks hosted in a hybrid cloud, which is an emerging cloud model thatintegrates private and public clouds to use the best of both worlds. Therefore,in this paper, we evaluate the performance of Hadoop, Spark, and Flink in ahybrid cloud in terms of execution time, resource utilization, horizontalscalability, vertical scalability, and cost. For this study, our hybrid cloudconsists of OpenStack (private cloud) and MS Azure (public cloud). We use bothbatch and iterative workloads for the evaluation. Our results show that in ahybrid cloud (i) the execution time increases as more nodes are borrowed by theprivate cloud from the public cloud, (ii) Flink outperforms Spark, which inturn outperforms Hadoop in terms of execution time, (iii) Hadoop transfers thelargest amount of data among the nodes during the workload execution whileSpark transfers the least amount of data, (iv) all three frameworkshorizontally scale better as compared to vertical scaling, and (v) Spark isfound to be least expensive in terms of $ cost for data processing while Hadoopis found the most expensive.

Evaluation of Distributed Data Processing Frameworks in Hybrid Clouds