Publication Details
Scheduling Decisions in Stream Processing on Heterogeneous Clusters
scheduling; resource-awareness; benchmarking; heterogeneous clusters; stream
processing; Apache Storm
Stream processing is a paradigm evolving in response to well-known limitations of
widely adopted MapReduce paradigm for big data processing, a hot topic of today's
computer world. Moreover, in the field of computation facilities, heterogeneity
of data processing clusters, intended or unintended, is starting to be relatively
common. This paper deals with scheduling problems and decisions in stream
processing on heterogeneous clusters. It brings an overview of current state of
the art of stream processing on heterogeneous clusters with focus on resource
allocation and scheduling. Basic scheduling decisions are discussed and
demonstrated on naive scheduling of a sample application. The paper presents
a proposal of a novel scheduler for stream processing frameworks on heterogeneous
clusters, which employs design-time knowledge as well as benchmarking techniques
to achieve optimal resource-aware deployment of applications over the clusters
and eventually better overall utilization of the cluster.
@inproceedings{BUT111552,
author="Marek {Rychlý} and Petr {Škoda} and Pavel {Smrž}",
title="Scheduling Decisions in Stream Processing on Heterogeneous Clusters",
booktitle="2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems",
year="2014",
pages="614--619",
publisher="IEEE Computer Society",
address="Birmingham",
doi="10.1109/CISIS.2014.94",
isbn="978-1-4799-4325-8",
url="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6915583"
}