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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An Energy-Aware Coflow Scheduling Approach for Sustainable Big Data Workload Management./
作者:	Ahmad, Sadiya.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	38 p.
附註:	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.
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