Data-Locality Aware Scientific Workflow Scheduling Methods in HPC Cloud Environments

Data-Locality Aware Scientific Workflow Scheduling Methods in HPC Cloud Environments Efficient data-aware methods in job scheduling, distributed storage management and data management platforms are necessary for successful execution of data-intensive applications. However, research about methods for data-intensive scientific applications are insufficient in large-scale distributed cloud and cluster computing environments and data-aware methods are becoming more complex. In this paper, we propose a Data-Locality Aware Workflow Scheduling (D-LAWS) technique and a locality-aware resource management method for data-intensive scientific workflows in HPC cloud environments. D-LAWS applies data-locality and data transfer time based on network bandwidth to scientific workflow task scheduling and balances resource utilization and parallelism of tasks at the node-level. Our method consolidates VMs and consider task parallelism by data flow during the planning of task executions of a data-intensive scientific workflow. We additionally consider more complex workflow models and data locality pertaining to the placement and transfer of data prior to task executions. We implement and validate the methods based on fairness in cloud environments. Experimental results show that, the proposed methods can improve performance and data-locality of data-intensive workflows in cloud environments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Parallel Programming Springer Journals

Data-Locality Aware Scientific Workflow Scheduling Methods in HPC Cloud Environments

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Publisher
Springer US
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Computer Science; Theory of Computation; Processor Architectures; Software Engineering/Programming and Operating Systems
ISSN
0885-7458
eISSN
1573-7640
D.O.I.
10.1007/s10766-016-0463-0
Publisher site
See Article on Publisher Site

Abstract

Efficient data-aware methods in job scheduling, distributed storage management and data management platforms are necessary for successful execution of data-intensive applications. However, research about methods for data-intensive scientific applications are insufficient in large-scale distributed cloud and cluster computing environments and data-aware methods are becoming more complex. In this paper, we propose a Data-Locality Aware Workflow Scheduling (D-LAWS) technique and a locality-aware resource management method for data-intensive scientific workflows in HPC cloud environments. D-LAWS applies data-locality and data transfer time based on network bandwidth to scientific workflow task scheduling and balances resource utilization and parallelism of tasks at the node-level. Our method consolidates VMs and consider task parallelism by data flow during the planning of task executions of a data-intensive scientific workflow. We additionally consider more complex workflow models and data locality pertaining to the placement and transfer of data prior to task executions. We implement and validate the methods based on fairness in cloud environments. Experimental results show that, the proposed methods can improve performance and data-locality of data-intensive workflows in cloud environments.

Journal

International Journal of Parallel ProgrammingSpringer Journals

Published: Sep 29, 2016

References

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