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PEEL: A Framework for Benchmarking Distributed Systems and Algorithms

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Performance Evaluation and Benchmarking for the Analytics Era (TPCTC 2017)

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During the last decade, a multitude of novel systems for scalable and distributed data processing has been proposed in both academia and industry. While there are published results of experimental evaluations for nearly all systems, it remains a challenge to objectively compare different system’s performance. It is thus imperative to enable and establish benchmarks for these systems. However, even if workloads and data sets or data generators are fixed, orchestrating and executing benchmarks can be a major obstacle. Worse, many systems come with hardware-dependent parameters that have to be tuned and spawn a diverse set of configuration files. This impedes portability and reproducibility of benchmarks. To address these problems and to foster reproducible and portable experiments and benchmarks of distributed data processing systems, we present PEEL, a framework to define, execute, analyze, and share experiments. PEEL enables the transparent specification of benchmarking workloads and system configuration parameters. It orchestrates the systems involved and automatically runs and collects all associated logs of experiments. PEEL currently supports Apache HDFS, Hadoop, Flink, and Spark and can easily be extended to include further systems.

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This work has been supported through grants by the German Science Foundation (DFG) as FOR1306 Stratosphere, the German Ministry for Education and Research as Berlin Big Data Center BBDC (funding mark 01IS14013A) and by Oracle Labs.

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Correspondence to Christoph Boden .

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Boden, C., Alexandrov, A., Kunft, A., Rabl, T., Markl, V. (2018). PEEL: A Framework for Benchmarking Distributed Systems and Algorithms. In: Nambiar, R., Poess, M. (eds) Performance Evaluation and Benchmarking for the Analytics Era. TPCTC 2017. Lecture Notes in Computer Science(), vol 10661. Springer, Cham.

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  • Print ISBN: 978-3-319-72400-3

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