東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Cooperative Batch Scheduling for HPC...

Yang, Xu.

Linked to FindBook

Google Book

Amazon

博客來

Cooperative Batch Scheduling for HPC Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Cooperative Batch Scheduling for HPC Systems./
Author:	Yang, Xu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	138 p.
Notes:	Source: Dissertations Abstracts International, Volume: 79-01, Section: B.
Contained By:	Dissertations Abstracts International79-01B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10272878
ISBN:	9781369852912

Cooperative Batch Scheduling for HPC Systems.
Yang, Xu.

Cooperative Batch Scheduling for HPC Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 138 p.

Source: Dissertations Abstracts International, Volume: 79-01, Section: B.

Thesis (Ph.D.)--Illinois Institute of Technology, 2017.

This item must not be sold to any third party vendors.

The batch scheduler is an important system software serving as the interface between users and HPC systems. Users submit their jobs via batch scheduling portal and the batch scheduler makes scheduling decision for each job based on its request for system resources and system availability. Jobs submitted to HPC systems are usually parallel applications and their lifecycle consists of multiple running phases, such as computation, communication and input/output data. Thus, the running of such parallel applications could involve various system resources, such as power, network bandwidth, I/O bandwidth, storage, etc. And most of these system resources are shared among concurrently running jobs. However, Today's batch schedulers do not take the contention and interference between jobs over these resources into consideration for making scheduling decisions, which has been identified as one of the major culprits for both the system and application performance variability. In this work, we propose a cooperative batch scheduling framework for HPC systems. The motivation of our work is to take important factors about jobs and the system, such as job power, job communication characteristics and network topology, for making orchestrated scheduling decisions to reduce the contention between concurrently running jobs and to alleviate the performance variability. Our contributions are the design and implementation of several coordinated scheduling models and algorithms for addressing some chronic issues in HPC systems. The proposed models and algorithms in this work have been evaluated by the means of simulation using workload traces and application communication traces collected from production HPC systems. Preliminary experimental results show that our models and algorithms can effectively improve the application and the system overall performance, HPC facilities' operation cost, and alleviate the performance variability caused by job interference.

ISBN: 9781369852912Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Batch scheduling

Cooperative Batch Scheduling for HPC Systems.
LDR:03155nmm a2200373 4500 001 2267242
005 20200623064714.5
008 220629s2017 ||||||||||||||||| ||eng d
020 $a 9781369852912
035 $a (MiAaPQ)AAI10272878
035 $a (MiAaPQ)iit:10552
035 $a AAI10272878
040 $a MiAaPQ $c MiAaPQ
100 1 $a Yang, Xu. $3 1005625
245 1 0 $a Cooperative Batch Scheduling for HPC Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 138 p.
500 $a Source: Dissertations Abstracts International, Volume: 79-01, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Lan, Zhiling.
502 $a Thesis (Ph.D.)--Illinois Institute of Technology, 2017.
506 $a This item must not be sold to any third party vendors.
520 $a The batch scheduler is an important system software serving as the interface between users and HPC systems. Users submit their jobs via batch scheduling portal and the batch scheduler makes scheduling decision for each job based on its request for system resources and system availability. Jobs submitted to HPC systems are usually parallel applications and their lifecycle consists of multiple running phases, such as computation, communication and input/output data. Thus, the running of such parallel applications could involve various system resources, such as power, network bandwidth, I/O bandwidth, storage, etc. And most of these system resources are shared among concurrently running jobs. However, Today's batch schedulers do not take the contention and interference between jobs over these resources into consideration for making scheduling decisions, which has been identified as one of the major culprits for both the system and application performance variability. In this work, we propose a cooperative batch scheduling framework for HPC systems. The motivation of our work is to take important factors about jobs and the system, such as job power, job communication characteristics and network topology, for making orchestrated scheduling decisions to reduce the contention between concurrently running jobs and to alleviate the performance variability. Our contributions are the design and implementation of several coordinated scheduling models and algorithms for addressing some chronic issues in HPC systems. The proposed models and algorithms in this work have been evaluated by the means of simulation using workload traces and application communication traces collected from production HPC systems. Preliminary experimental results show that our models and algorithms can effectively improve the application and the system overall performance, HPC facilities' operation cost, and alleviate the performance variability caused by job interference.
590 $a School code: 0091.
650 4 $a Computer science. $3 523869
653 $a Batch scheduling
653 $a Contention
653 $a High performance computing
653 $a Interference
653 $a Network topology
690 $a 0984
710 2 $a Illinois Institute of Technology. $b Computer Science. $3 2094784
773 0 $t Dissertations Abstracts International $g 79-01B.
790 $a 0091
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10272878