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

Linked to FindBook

Google Book

Amazon

博客來

An Energy-Aware Coflow Scheduling Approach for Sustainable Big Data Workload Management.

Record Type:	Electronic resources : Monograph/item
Title/Author:	An Energy-Aware Coflow Scheduling Approach for Sustainable Big Data Workload Management./
Author:	Ahmad, Sadiya.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	38 p.
Notes:	Source: Masters Abstracts International, Volume: 83-02.
Contained By:	Masters Abstracts International83-02.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28541985
ISBN:	9798534663433

An Energy-Aware Coflow Scheduling Approach for Sustainable Big Data Workload Management.
Ahmad, Sadiya.

An Energy-Aware Coflow Scheduling Approach for Sustainable Big Data Workload Management. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 38 p.

Source: Masters Abstracts International, Volume: 83-02.

Thesis (M.S.)--Saint Louis University, 2021.

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

The growth of data-centered communication has necessitated the development of time-efficient methods, such as the coflow. The central idea behind the coflow concept is that computational goals in cluster computing rely on the completion of multiple flows. The coflow abstraction optimizes performance by grouping flows on the basis of collective objectives. Previous research has shown that coflow scheduling can significantly improve communication performance, reduce completion time, and increase the number of jobs meeting deadlines in big-data workloads and distributed parallel applications. In this way, communication performance has been mostly explored as a reduction in computational time. This project extends previous work by investigating how coflow scheduling can also reduce energy consumption. Various schedulers have been proposed to improve efficiency in big-data workloads. However, these algorithms do not consider energy consumption. Energy consumption of data centers, however, continues to increase, with data centers now consuming more than 1% of global energy use. This thesis proposes an energy-efficient algorithm to reduce these energy demands and introduces a simulator for coflow scheduling algorithms. The performance of popular algorithms is compared on the basis of energy and CPU time. Our findings indicate that the proposed Energy Consumption Efficient (ECE) algorithm can reduce energy consumption with moderate loss to computational completion time.

ISBN: 9798534663433Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Data workload management

An Energy-Aware Coflow Scheduling Approach for Sustainable Big Data Workload Management.
LDR:02631nmm a2200373 4500 001 2344642
005 20220531064614.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798534663433
035 $a (MiAaPQ)AAI28541985
035 $a AAI28541985
040 $a MiAaPQ $c MiAaPQ
100 1 $a Ahmad, Sadiya. $3 3683426
245 1 3 $a An Energy-Aware Coflow Scheduling Approach for Sustainable Big Data Workload Management.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 38 p.
500 $a Source: Masters Abstracts International, Volume: 83-02.
500 $a Advisor: Esposito, Flavio.
502 $a Thesis (M.S.)--Saint Louis University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The growth of data-centered communication has necessitated the development of time-efficient methods, such as the coflow. The central idea behind the coflow concept is that computational goals in cluster computing rely on the completion of multiple flows. The coflow abstraction optimizes performance by grouping flows on the basis of collective objectives. Previous research has shown that coflow scheduling can significantly improve communication performance, reduce completion time, and increase the number of jobs meeting deadlines in big-data workloads and distributed parallel applications. In this way, communication performance has been mostly explored as a reduction in computational time. This project extends previous work by investigating how coflow scheduling can also reduce energy consumption. Various schedulers have been proposed to improve efficiency in big-data workloads. However, these algorithms do not consider energy consumption. Energy consumption of data centers, however, continues to increase, with data centers now consuming more than 1% of global energy use. This thesis proposes an energy-efficient algorithm to reduce these energy demands and introduces a simulator for coflow scheduling algorithms. The performance of popular algorithms is compared on the basis of energy and CPU time. Our findings indicate that the proposed Energy Consumption Efficient (ECE) algorithm can reduce energy consumption with moderate loss to computational completion time.
590 $a School code: 0193.
650 4 $a Computer science. $3 523869
650 4 $a Information technology. $3 532993
650 4 $a Information science. $3 554358
650 4 $a Software. $2 gtt. $3 619355
650 4 $a Communication. $3 524709
650 4 $a Electricity distribution. $3 3562889
650 4 $a Bandwidths. $3 3560998
650 4 $a Data processing. $3 680224
650 4 $a Energy consumption. $3 631630
650 4 $a Traffic congestion. $3 706812
650 4 $a Schedules. $3 3564128
650 4 $a Scheduling. $3 750729
650 4 $a Simulation. $3 644748
650 4 $a Electricity. $3 524507
650 4 $a Power. $3 518736
650 4 $a Experiments. $3 525909
650 4 $a Decision making. $3 517204
650 4 $a Energy efficiency. $3 3555643
650 4 $a Algorithms. $3 536374
653 $a Data workload management
653 $a Energy-aware
653 $a Scheduling approach
653 $a Energy consumption
690 $a 0984
690 $a 0489
690 $a 0723
690 $a 0459
710 2 $a Saint Louis University. $b Computer Science. $3 3558089
773 0 $t Masters Abstracts International $g 83-02.
790 $a 0193
791 $a M.S.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28541985