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

Challenges, Opportunities, and Solut...

Thomas, Shelby.

Linked to FindBook

Google Book

Amazon

博客來

Challenges, Opportunities, and Solutions for Next Generation Data Analytics Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Challenges, Opportunities, and Solutions for Next Generation Data Analytics Systems./
Author:	Thomas, Shelby.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	116 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-07, Section: B.
Contained By:	Dissertations Abstracts International82-07B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28091497
ISBN:	9798557039239

Challenges, Opportunities, and Solutions for Next Generation Data Analytics Systems.
Thomas, Shelby.

Challenges, Opportunities, and Solutions for Next Generation Data Analytics Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 116 p.

Source: Dissertations Abstracts International, Volume: 82-07, Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2020.

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

Data on the internet is increasing at an exponential rate. The arms race to build and capitalize on this information has led to an increase in raw horsepower thrown at the problem. Datacenters have gotten larger in both size and in number, hardware has been built that promises 10x improvement over the previous generation's model and software frameworks have been amended and repurposed rather than redesigned. This has led to diluted returns on investments when it comes to performance. Large improvements on just one part of a system often do not translate directly to others. This culminates with wasted cores, cycles, and costs. In this thesis I look at new ways to design for the data problem in a holistic way. I analyze current trends in CPUs, network interconnects, and software frameworks and find opportunities where existing systems can improve by working in tandem.In the case of network speeds, I surface nuances around the interaction of the network card, memory, and application. I discovered a new insight, Dark Packets, which shows that the outsized gains in network speeds cannot be taken advantage of without improvements to DRAM bandwidth or increased core counts. In the case of software frameworks, I find that kernel network stacks today are not suited to the fan-out architecture found in today's data analytic frameworks.Finally, I take the insights learned from these papers and build a new general purpose serverless burst-parallel data analytics framework, SAL, that is portable, general purpose, and performant. The goal of this work is to demonstrate that holistic full stack approaches to building data analytic systems results in lower cost and higher performance.

ISBN: 9798557039239Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Next generation

Challenges, Opportunities, and Solutions for Next Generation Data Analytics Systems.
LDR:02808nmm a2200349 4500 001 2284263
005 20211115072419.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798557039239
035 $a (MiAaPQ)AAI28091497
035 $a AAI28091497
040 $a MiAaPQ $c MiAaPQ
100 1 $a Thomas, Shelby. $3 3563420
245 1 0 $a Challenges, Opportunities, and Solutions for Next Generation Data Analytics Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 116 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-07, Section: B.
500 $a Advisor: Porter, George.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Data on the internet is increasing at an exponential rate. The arms race to build and capitalize on this information has led to an increase in raw horsepower thrown at the problem. Datacenters have gotten larger in both size and in number, hardware has been built that promises 10x improvement over the previous generation's model and software frameworks have been amended and repurposed rather than redesigned. This has led to diluted returns on investments when it comes to performance. Large improvements on just one part of a system often do not translate directly to others. This culminates with wasted cores, cycles, and costs. In this thesis I look at new ways to design for the data problem in a holistic way. I analyze current trends in CPUs, network interconnects, and software frameworks and find opportunities where existing systems can improve by working in tandem.In the case of network speeds, I surface nuances around the interaction of the network card, memory, and application. I discovered a new insight, Dark Packets, which shows that the outsized gains in network speeds cannot be taken advantage of without improvements to DRAM bandwidth or increased core counts. In the case of software frameworks, I find that kernel network stacks today are not suited to the fan-out architecture found in today's data analytic frameworks.Finally, I take the insights learned from these papers and build a new general purpose serverless burst-parallel data analytics framework, SAL, that is portable, general purpose, and performant. The goal of this work is to demonstrate that holistic full stack approaches to building data analytic systems results in lower cost and higher performance.
590 $a School code: 0033.
650 4 $a Computer science. $3 523869
650 4 $a Systems science. $3 3168411
650 4 $a Information science. $3 554358
650 4 $a Computer engineering. $3 621879
653 $a Next generation
653 $a Data analytics systems
690 $a 0984
690 $a 0723
690 $a 0790
690 $a 0464
710 2 $a University of California, San Diego. $b Computer Science and Engineering. $3 1018473
773 0 $t Dissertations Abstracts International $g 82-07B.
790 $a 0033
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28091497