Bench-ranking: a first step towards prescriptive performance analyses for big data frameworks

Ragab, Mohamed, Tommasini, Riccardo and Awaysheh, Feras (2021) Bench-ranking: a first step towards prescriptive performance analyses for big data frameworks. 2021 IEEE International Conference on Big Data (Big Data), , Virtual. 15 - 18 Dec 2021. (doi:10.1109/BigData52589.2021.9671277).

Abstract

Leveraging Big Data (BD) processing frameworks to process large-scale Resource Description Framework (RDF) datasets holds a great interest in optimizing query performance. Modern BD services are complicated data systems, where tuning the configurations notably affects the performance. Benchmarking different frameworks and configurations provides the community with best practices towards selecting the most suitable configurations. However, most of these benchmarking efforts are classified as descriptive or diagnostic analytics. Moreover, there is no standardization for comparing and contrasting these benchmarks based on quantitative ranking techniques. This paper aims to fill this timely research gap by proposing ranking criteria (called Bench-ranking) that provide prescriptive analytics via ranking functions. In particular, Bench-ranking starts by describing the current state-of-the-art single-dimensional …

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