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Optimization of data-intensive workflows in stream-based data processing models

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Abstract

Stream computing applications require minimum latency and high throughput for efficiently processing real-time data. Typically, data-intensive applications where large datasets are required to be moved across execution nodes have low latency requirements. In this paper, a stream-based data processing model is adopted to develop an algorithm for optimal partitioning the input data such that the inter-partition data flow remains minimal. The proposed algorithm improves the execution of the data-intensive workflows in heterogeneous computing environments by partitioning the data-intensive workflow and mapping each partition on the available heterogeneous resources that offer minimum execution time. Minimum data movement between the partitions reduces the latency, which can be further reduced by applying advanced data parallelism techniques. In this paper, we apply data parallelism technique to the bottleneck (most compute-intensive) task in each partition that significantly reduces the latency. We study the effectiveness and the performance of the proposed approach by using synthesized workflows and real-world applications, such as Montage and Cybershake. Our evaluation shows that the proposed algorithm provides schedules with approximately 12% reduced latency and nearly 17% enhanced throughput as compared to the existing state of the art algorithms.

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Notes

  1. https://pegasus.isi.edu/overview/.

  2. https://confluence.pegasus.isi.edu/display/pegasus/Workflow+Data.

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Acknowledgements

The work presented in this paper is partly supported by the Ministry of Education Malaysia (FRGS FP051-2013A and UMRG RP001F-13ICT).

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Saima Gulzar Ahmad & Chee Sun Liew

  2. IBM Research, Mulhuddart, Ireland

    M. Mustafa Rafique

  3. Department of Computer Science, COMSATS Institute of Information Technology, Wah Cantt, Pakistan

    Ehsan Ullah Munir

Authors
  1. Saima Gulzar Ahmad
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  2. Chee Sun Liew
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  3. M. Mustafa Rafique
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  4. Ehsan Ullah Munir
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Correspondence to Chee Sun Liew.

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Ahmad, S.G., Liew, C.S., Rafique, M.M. et al. Optimization of data-intensive workflows in stream-based data processing models. J Supercomput 73, 3901–3923 (2017). https://doi.org/10.1007/s11227-017-1991-0

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  • DOI: https://doi.org/10.1007/s11227-017-1991-0

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