國立虎尾科技大學 |

Framework for Large Data Processing under Constrained Resources.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Framework for Large Data Processing under Constrained Resources./
Author:	Wu, Rui.
Description:	1 online resource (124 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780438185791

Framework for Large Data Processing under Constrained Resources.
Wu, Rui.

Framework for Large Data Processing under Constrained Resources. - 1 online resource (124 pages)

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--University of Nevada, Reno, 2018.

Includes bibliographical references

Data processing is used to uncover, transform, and classify information inside of data. Data-intensive research topics, such as environmental parameter prediction and sensor data imputation, require abundant computing power. To process big data efficiently, a server cluster is used for most cases. On one hand, a more powerful server cluster should be better. On the other hand, the powerful cluster will require a greater budget. "How to balance this tradeoff" is a challenge. Another challenge is how to improve communication between different nodes in a server cluster. The communication is usually through network and transportation speed is very slow.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438185791Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Framework for Large Data Processing under Constrained Resources.
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100 1 $a Wu, Rui. $3 1190526
245 1 0 $a Framework for Large Data Processing under Constrained Resources.
264 0 $c 2018
300 $a 1 online resource (124 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 $a Advisers: Sergiu Dascalu; Frederick C. Harris.
502 $a Thesis (Ph.D.)--University of Nevada, Reno, 2018.
504 $a Includes bibliographical references
520 $a Data processing is used to uncover, transform, and classify information inside of data. Data-intensive research topics, such as environmental parameter prediction and sensor data imputation, require abundant computing power. To process big data efficiently, a server cluster is used for most cases. On one hand, a more powerful server cluster should be better. On the other hand, the powerful cluster will require a greater budget. "How to balance this tradeoff" is a challenge. Another challenge is how to improve communication between different nodes in a server cluster. The communication is usually through network and transportation speed is very slow.
520 $a In this thesis, we propose a data processing framework that can provide stable service with a limited budget. "Stable" service means the average waiting time and queue length do not change massively. The key of this framework control strategy is to import budget and local server computing power concepts into the M/M/1/1/infinity/infinity queue model. To tackle the data communication challenge, data is compressed before transportation and decompressed when it arrives at its destination. An improved compression algorithm is proposed for this data transportation workflow, which leverages multiple GPUs and, to the best of our knowledge, is much faster than most other algorithms. Three data processing services that rely on the proposed framework are also presented in detail, to illustrate and prove the capabilities of our solution.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0464
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Nevada, Reno. $b Computer Engineering. $3 1190527
773 0 $t Dissertation Abstracts International $g 79-12B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10824178 $z click for full text (PQDT)