Title
Generating synthetic task graphs for simulating stream computing systems
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.
Year
DOI
Venue
2013
10.1016/j.jpdc.2013.06.002
J. Parallel Distrib. Comput.
Keywords
Field
DocType
key property,realistic input stream graph,general stream-computing system,domain-specific fine-tuning,generating synthetic task graph,large-scale workloads,synthetic generation,computational model,stream-computing application,random graph generation concept,graph generation technique,simulating stream computing system
Graph,Graph generation,Random graph,Computer science,Parallel computing,Stream,Theoretical computer science,Distributed computing
Journal
Volume
Issue
ISSN
73
10
0743-7315
Citations 
PageRank 
References 
6
0.42
19
Authors
7
Name
Order
Citations
PageRank
Deepak Ajwani118822.30
Shoukat Ali297960.87
Kostas Katrinis310219.41
Cheng-Hong Li4795.98
Alfred J. Park5354.53
John P. Morrison626245.28
Eugen Schenfeld729638.01