Detail publikace

Scheduling Decisions in Stream Processing on Heterogeneous Clusters

RYCHLÝ, M. ŠKODA, P. SMRŽ, P.

Originální název

Scheduling Decisions in Stream Processing on Heterogeneous Clusters

Anglický název

Scheduling Decisions in Stream Processing on Heterogeneous Clusters

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

BibTex


@inproceedings{BUT111552,
  author="Marek {Rychlý} and Petr {Škoda} and Pavel {Smrž}",
  title="Scheduling Decisions in Stream Processing on Heterogeneous Clusters",
  annote="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.",
  address="IEEE Computer Society",
  booktitle="2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems",
  chapter="111552",
  doi="10.1109/CISIS.2014.94",
  edition="NEUVEDEN",
  howpublished="electronic, physical medium",
  institution="IEEE Computer Society",
  year="2014",
  month="july",
  pages="614--619",
  publisher="IEEE Computer Society",
  type="conference paper"
}