Heterogeneity-Aware Scheduler for Stream Processing Frameworks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F15%3APU116935" target="_blank" >RIV/00216305:26230/15:PU116935 - isvavai.cz</a>
Result on the web
<a href="http://www.inderscience.com/info/inarticle.php?artid=69090" target="_blank" >http://www.inderscience.com/info/inarticle.php?artid=69090</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1504/IJBDI.2015.069090" target="_blank" >10.1504/IJBDI.2015.069090</a>
Alternative languages
Result language
angličtina
Original language name
Heterogeneity-Aware Scheduler for Stream Processing Frameworks
Original language description
This article discusses problems and decisions related to scheduling of stream processing applications in heterogeneous clusters. An overview of the current state of the art of the stream processing on heterogeneous clusters with a focus on resource allocation and scheduling is presented first. Then, common scheduling approaches of various stream processing frameworks are discussed and their limited applicability in the heterogeneous environment is demonstrated on a simple stream application. Finally, the article presents a novel heterogeneity-aware scheduler for the stream processing frameworks based on design-time knowledge as well as benchmarking techniques. It is shown that the scheduler overcomes alternatives in resource-aware deployment over cluster nodes and thus it leads to a better utilisation of the clusters.
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Big Data Intelligence
ISSN
2053-1397
e-ISSN
—
Volume of the periodical
2
Issue of the periodical within the volume
2
Country of publishing house
GB - UNITED KINGDOM
Number of pages
11
Pages from-to
70-80
UT code for WoS article
—
EID of the result in the Scopus database
—