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Cross-Scenario Performance Modelling for Big Data Ecosystems

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Artificial Intelligence in HCI (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12217))

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Abstract

Performance prediction is an essential aspect of several critical system design decisions, such as workload scheduling and resource planning. However, developing a model with higher prediction accuracy is a challenging task in big data systems due to the stack complexity and environmental heterogeneity. Workload modelling aims to simplify the connection between workloads factors and performance testing. Most of the workload models rely on a single scenario under test (SUT) method, where the trained and the evaluated data have the same distribution. However, a single SUT is not the ideal modelling method for big data workloads, as SUTs change frequently. Big data systems have a considerable amount of possible test scenarios that are generated from changing one or more elements in the testing environment, such as changing benchmarks, software versions, or cloud service types. To address this issue, we propose a cross-Scenario workload modelling method that aims to improve the workloads’ performance classification accuracy. The proposed approach adopts the Transfer Learning concept for reusing models cross different but related scenarios. In this work, we evaluate the proposed approach on multi real-world scenarios in Hadoop which is an example of big data system. The empirical results showed that the proposed approach is more accurate than SUT method.

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Authors and Affiliations

  1. Department of Computer Science, Ryerson University, Toronto, Canada

    Fatimah Alsayoud & Ali Miri

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  1. Fatimah Alsayoud
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  2. Ali Miri
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Correspondence to Ali Miri .

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Editors and Affiliations

  1. Siemens, Princeton, USA

    Helmut Degen

  2. University of Central Florida, Orlando, FL, USA

    Lauren Reinerman-Jones

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Alsayoud, F., Miri, A. (2020). Cross-Scenario Performance Modelling for Big Data Ecosystems. In: Degen, H., Reinerman-Jones, L. (eds) Artificial Intelligence in HCI. HCII 2020. Lecture Notes in Computer Science(), vol 12217. Springer, Cham. https://doi.org/10.1007/978-3-030-50334-5_14

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  • DOI: https://doi.org/10.1007/978-3-030-50334-5_14

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