Generating synthetic task graphs for simulating stream computing systems

Files in This Item:
File Description SizeFormat 
ajwani_jpdc13.pdf538.53 kBAdobe PDFDownload
Title: Generating synthetic task graphs for simulating stream computing systems
Authors: Ajwani, Deepak
Ali, Shoukat
Katrinis, Kostas
et al.
Permanent link:
Date: Oct-2013
Online since: 2019-04-10T11:47:32Z
Abstract: Stream-computing is an emerging computational model for performing complex operations on and across multi-source, high-volume data flows. The pool of mature publicly available applications employing this model is fairly small, and therefore the availability of workloads for various types of applications is scarce. Thus, there is a need for synthetic generation of large-scale workloads to drive simulations and estimate the performance of stream-computing applications at scale. We identify the key properties shared by most task graphs of stream-computing applications and use them to extend known random graph generation concepts with stream computing specific features, providing researchers with realistic input stream graphs. Our graph generation techniques serve the purpose of covering a disparity of potential applications and user input. Our first "domain-specific" framework exhibits high user-controlled configurability while the second "application- agnostic" framework focuses solely on emulating the key properties of general stream-computing systems, at the loss of domain-specific fine-tuning. © 2013 Elsevier Inc. All rights reserved.
Type of material: Journal Article
Publisher: Elsevier
Journal: Journal of Parallel and Distributed Computing
Volume: 73
Issue: 10
Start page: 1362
End page: 1374
Copyright (published version): 2013 Elsevier
Keywords: Stream computing systemsWorkload characterizationComputational task graphsSynthetic stream-computing graphs
DOI: 10.1016/j.jpdc.2013.06.002
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection

Show full item record

Citations 50

Last Week
Last month
checked on May 24, 2019

Google ScholarTM



This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.