Standard

Profiling Scheduler for Efficient Resource Utilization. / Bogdanov, A.; Gaiduchok, V.; Ahmed, N.; Cubahiro, A.; Gankevich, I.

Computational Science and Its Applications -- ICCSA 2015. Springer Nature, 2015. p. 299-310.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research

Harvard

Bogdanov, A, Gaiduchok, V, Ahmed, N, Cubahiro, A & Gankevich, I 2015, Profiling Scheduler for Efficient Resource Utilization. in Computational Science and Its Applications -- ICCSA 2015. Springer Nature, pp. 299-310, 15th International Conference on Computational Science and Its Applications, ICCSA 2015, Banff, Canada, 21/06/15. https://doi.org/10.1007/978-3-319-21410-8_23

APA

Bogdanov, A., Gaiduchok, V., Ahmed, N., Cubahiro, A., & Gankevich, I. (2015). Profiling Scheduler for Efficient Resource Utilization. In Computational Science and Its Applications -- ICCSA 2015 (pp. 299-310). Springer Nature. https://doi.org/10.1007/978-3-319-21410-8_23

Vancouver

Bogdanov A, Gaiduchok V, Ahmed N, Cubahiro A, Gankevich I. Profiling Scheduler for Efficient Resource Utilization. In Computational Science and Its Applications -- ICCSA 2015. Springer Nature. 2015. p. 299-310 https://doi.org/10.1007/978-3-319-21410-8_23

Author

Bogdanov, A. ; Gaiduchok, V. ; Ahmed, N. ; Cubahiro, A. ; Gankevich, I. / Profiling Scheduler for Efficient Resource Utilization. Computational Science and Its Applications -- ICCSA 2015. Springer Nature, 2015. pp. 299-310

BibTeX

@inproceedings{948b272fafe84877bd5222f609292557,
title = "Profiling Scheduler for Efficient Resource Utilization",
abstract = "Optimal resource utilization is one of the most important and most challenging tasks for computational centers. A typical contemporary center includes several clusters. These clusters are used by many clients. So, administrators should set resource sharing policies that will meet different requirements of different groups of users. Users want to compute their tasks fast while organizations want their resources to be utilized efficiently. Traditional schedulers do not allow administrator to efficiently solve these problems in that way. Dynamic resource reallocation can improve the efficiency of system utilization while profiling running applications can generate important statistical data that can be used in order to optimize future application usage. These are basic advantages of a new scheduler that are discussed in this paper.",
keywords = "Computational cluster, Scheduler, HPC, Profiling, Resource sharing, Load balancing, Networking",
author = "A. Bogdanov and V. Gaiduchok and N. Ahmed and A. Cubahiro and I. Gankevich",
year = "2015",
doi = "10.1007/978-3-319-21410-8_23",
language = "English",
isbn = "9783319214092",
pages = "299--310",
booktitle = "Computational Science and Its Applications -- ICCSA 2015",
publisher = "Springer Nature",
address = "Germany",
note = "15th International Conference on Computational Science and Its Applications, ICCSA 2015 ; Conference date: 21-06-2015 Through 24-06-2015",

}

RIS

TY - GEN

T1 - Profiling Scheduler for Efficient Resource Utilization

AU - Bogdanov, A.

AU - Gaiduchok, V.

AU - Ahmed, N.

AU - Cubahiro, A.

AU - Gankevich, I.

PY - 2015

Y1 - 2015

N2 - Optimal resource utilization is one of the most important and most challenging tasks for computational centers. A typical contemporary center includes several clusters. These clusters are used by many clients. So, administrators should set resource sharing policies that will meet different requirements of different groups of users. Users want to compute their tasks fast while organizations want their resources to be utilized efficiently. Traditional schedulers do not allow administrator to efficiently solve these problems in that way. Dynamic resource reallocation can improve the efficiency of system utilization while profiling running applications can generate important statistical data that can be used in order to optimize future application usage. These are basic advantages of a new scheduler that are discussed in this paper.

AB - Optimal resource utilization is one of the most important and most challenging tasks for computational centers. A typical contemporary center includes several clusters. These clusters are used by many clients. So, administrators should set resource sharing policies that will meet different requirements of different groups of users. Users want to compute their tasks fast while organizations want their resources to be utilized efficiently. Traditional schedulers do not allow administrator to efficiently solve these problems in that way. Dynamic resource reallocation can improve the efficiency of system utilization while profiling running applications can generate important statistical data that can be used in order to optimize future application usage. These are basic advantages of a new scheduler that are discussed in this paper.

KW - Computational cluster

KW - Scheduler

KW - HPC

KW - Profiling

KW - Resource sharing

KW - Load balancing

KW - Networking

U2 - 10.1007/978-3-319-21410-8_23

DO - 10.1007/978-3-319-21410-8_23

M3 - Conference contribution

SN - 9783319214092

SP - 299

EP - 310

BT - Computational Science and Its Applications -- ICCSA 2015

PB - Springer Nature

T2 - 15th International Conference on Computational Science and Its Applications, ICCSA 2015

Y2 - 21 June 2015 through 24 June 2015

ER -

ID: 3943819