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

Optimization of data-intensive workflows in stream-based data processing models 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Supercomputing Springer Journals

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

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Programming Languages, Compilers, Interpreters; Processor Architectures; Computer Science, general
ISSN
0920-8542
eISSN
1573-0484
D.O.I.
10.1007/s11227-017-1991-0
Publisher site
See Article on Publisher Site

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.

Journal

The Journal of SupercomputingSpringer Journals

Published: Mar 8, 2017

References

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