A Metaheuristic for Optimizing the Performance and the Fairness
in Job Scheduling Systems

D 2015

A Metaheuristic for Optimizing the Performance and the Fairness in Job Scheduling Systems

KLUSÁČEK, Dalibor and Hana RUDOVÁ

Basic information

Original name

A Metaheuristic for Optimizing the Performance and the Fairness in Job Scheduling Systems

Authors

KLUSÁČEK, Dalibor (203 Czech Republic, belonging to the institution) and Hana RUDOVÁ (203 Czech Republic, guarantor, belonging to the institution)

Edition

Germany, Artificial-Intelligence Applications in Information and Communication Technologies, p. 3-29, 27 pp. 2015

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

URL

RIV identification code

RIV/00216224:14330/15:00081127

Organization unit

Faculty of Informatics

ISBN

978-3-319-19832-3

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-19833-0_1

UT WoS

000377199200002

Keywords in English

Job scheduling; Metaheuristic; Optimization; Fairness

Abstract

V originále

Many studies in the past two decades focused on the problem of efficient resource management and job scheduling in large computational systems such as HPC clusters and Grids. For this purpose, the application of Artificial Intelligence-based methods such as metaheuristics has been proposed in many works. This chapter provides an overview of such works that involve metaheuristics and discusses why mainstream resource management and scheduling systems are instead using only a limited set of rather simple scheduling policies. We identify several reasons that are causing this situation, e.g., a common use of overly simplified problem definitions with rather naive job and machine models or an application of unrealistic optimization criteria. In order to solve aforementioned issues, this chapter proposes new complex and well designed approaches that involve the use of metaheuristic which periodically optimizes job scheduling plan using several real life based optimization criteria. Importantly, approaches described in this chapter are successfully used in practice, i.e., within a production job scheduler which manages the computing infrastructure of the Czech Centre for Education, Research and Innovation in ICT (CERIT Scientific Cloud).

Links

GAP202/12/0306, research and development project

Name: Dyschnet - Dynamické plánování a rozvrhování výpočetních a síťových zdrojů (Acronym: Dyschnet)

Investor: Czech Science Foundation

Citovat

KLUSÁČEK, Dalibor and Hana RUDOVÁ. A Metaheuristic for Optimizing the Performance and the Fairness in Job Scheduling Systems. In Yacine Laalaoui, Nizar Bouguila. Artificial-Intelligence Applications in Information and Communication Technologies. Germany: Springer, 2015, p. 3-29. ISBN 978-3-319-19832-3. Available from: https://dx.doi.org/10.1007/978-3-319-19833-0_1.

@inproceedings{1313998,
   author = {Klusáček, Dalibor and Rudová, Hana},
   address = {Germany},
   booktitle = {Artificial-Intelligence Applications in Information and Communication Technologies},
   doi = {http://dx.doi.org/10.1007/978-3-319-19833-0_1},
   editor = {Yacine Laalaoui, Nizar Bouguila},
   keywords = {Job scheduling; Metaheuristic; Optimization; Fairness},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Germany},
   isbn = {978-3-319-19832-3},
   pages = {3-29},
   publisher = {Springer},
   title = {A Metaheuristic for Optimizing the Performance and the Fairness in Job Scheduling Systems},
   url = {http://link.springer.com/chapter/10.1007%2F978-3-319-19833-0_1},
   year = {2015}
}

TY  - JOUR
ID  - 1313998
AU  - Klusáček, Dalibor - Rudová, Hana
PY  - 2015
TI  - A Metaheuristic for Optimizing the Performance and the Fairness in Job Scheduling Systems
PB  - Springer
CY  - Germany
SN  - 9783319198323
KW  - Job scheduling
KW  - Metaheuristic
KW  - Optimization
KW  - Fairness
UR  - http://link.springer.com/chapter/10.1007%2F978-3-319-19833-0_1
L2  - http://link.springer.com/chapter/10.1007%2F978-3-319-19833-0_1
N2  - Many studies in the past two decades focused on the problem of efficient resource management and job scheduling in large computational systems such as HPC clusters and Grids. For this purpose, the application of Artificial Intelligence-based methods such as metaheuristics has been proposed in many works. This chapter provides an overview of such works that involve metaheuristics and discusses why mainstream resource management and scheduling systems are instead using only a limited set of rather simple scheduling policies. We identify several reasons that are causing this situation, e.g., a common use of overly simplified problem definitions with rather naive job and machine models or an application of unrealistic optimization criteria. In order to solve aforementioned issues, this chapter proposes new complex and well designed approaches that involve the use of metaheuristic which periodically optimizes job scheduling plan using several real life based optimization criteria. Importantly, approaches described in this chapter are successfully used in practice, i.e., within a production job scheduler which manages the computing infrastructure of the Czech Centre for Education, Research and Innovation in ICT (CERIT Scientific Cloud).
ER  -

KLUSÁČEK, Dalibor and Hana RUDOVÁ. A Metaheuristic for Optimizing the Performance and the Fairness in Job Scheduling Systems. In Yacine Laalaoui, Nizar Bouguila. \textit{Artificial-Intelligence Applications in Information and Communication Technologies}. Germany: Springer, 2015, p.~3-29. ISBN~978-3-319-19832-3. Available from: https://dx.doi.org/10.1007/978-3-319-19833-0\_{}1.

Detailed Information on Publication Record