國立虎尾科技大學 |

Resource Management for Advanced Data Analytics at Large Scale.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Resource Management for Advanced Data Analytics at Large Scale./
作者:	Zhang, Haoyu.
面頁冊數:	1 online resource (149 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780438049635

Resource Management for Advanced Data Analytics at Large Scale.
Zhang, Haoyu.

Resource Management for Advanced Data Analytics at Large Scale. - 1 online resource (149 pages)

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Ph.D.)--Princeton University, 2018.

Includes bibliographical references

The rapidly growing size of data and the complexity of analytics present new challenges for large-scale data analytics systems. Modern distributed computing frameworks need to support not only embarrassingly parallelizable batch jobs, but also advanced applications analyzing text and multimedia data using complex queries and machine learning (ML) models. Given the computation and storage costs of advanced data analytics, resource management is crucial. New applications and workloads expose vastly different characteristics which make traditional scheduling systems inadequate, and at the same time offer great opportunities that lead to new system designs for better performance.

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

Mode of access: World Wide Web

ISBN: 9780438049635Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Resource Management for Advanced Data Analytics at Large Scale.
LDR:03857ntm a2200385Ki 4500 001 919211
005 20181116131021.5
006 m o u
007 cr mn||||a|a||
008 190606s2018 xx obm 000 0 eng d
020 $a 9780438049635
035 $a (MiAaPQ)AAI10823767
035 $a (MiAaPQ)princeton:12602
035 $a AAI10823767
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Zhang, Haoyu. $3 1193727
245 1 0 $a Resource Management for Advanced Data Analytics at Large Scale.
264 0 $c 2018
300 $a 1 online resource (149 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: 79-10(E), Section: B.
500 $a Adviser: Michael J. Freedman.
502 $a Thesis (Ph.D.)--Princeton University, 2018.
504 $a Includes bibliographical references
520 $a The rapidly growing size of data and the complexity of analytics present new challenges for large-scale data analytics systems. Modern distributed computing frameworks need to support not only embarrassingly parallelizable batch jobs, but also advanced applications analyzing text and multimedia data using complex queries and machine learning (ML) models. Given the computation and storage costs of advanced data analytics, resource management is crucial. New applications and workloads expose vastly different characteristics which make traditional scheduling systems inadequate, and at the same time offer great opportunities that lead to new system designs for better performance.
520 $a In this thesis, we present resource management systems that significantly improve cloud resource efficiency by leveraging the specific characteristics of advanced data analytics applications. We present the design and implementation of the following systems:
520 $a (i) VideoStorm: a video analytics system that scales to process thousands of vision queries on live video streams over large clusters. VideoStorm's offline profiler generates resource-quality profiles for vision queries, and its online scheduler allocates resources to maximize performance in terms of vision processing quality and lag.
520 $a (ii) SLAQ: a cluster scheduling system for approximate ML training jobs that aims to maximize the overall model quality. In iterative and exploratory training settings, better models can be obtained faster by directing resources to jobs with the most potential for improvement. SLAQ allocates resources to maximize the cluster-wide quality improvement based on highly-tailored model quality predictions.
520 $a (iii) Riffle: an optimized shuffle service for big-data analytics frameworks that significantly improves I/O efficiency. The all-to-all data transfer (i.e., shuffle) in modern big-data systems (such as Spark and Hadoop) becomes the scaling bottleneck for multi-stage analytics jobs, due to the superlinear increase in disk I/O operations as data volume grows. Riffle boosts system performance by merging fragmented intermediate files and efficiently scheduling the merge operations.
520 $a Taken together, this thesis demonstrates a novel set of methods in both job-level and task-level scheduling for building scalable, highly-efficient, and cost-effective resource management systems. We have performed extensive evaluation with real production workloads, and our results show significant improvement in resource efficiency, job completion time, and system throughput for advanced data analytics.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Princeton University. $b Computer Science. $3 1179801
773 0 $t Dissertation Abstracts International $g 79-10B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10823767 $z click for full text (PQDT)