語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Optimized Cloud Based Scheduling
~
Tan, Rong Kun Jason.
Optimized Cloud Based Scheduling
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Optimized Cloud Based Scheduling/ by Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu.
作者:
Tan, Rong Kun Jason.
其他作者:
Leong, John A.
面頁冊數:
XIII, 99 p. 33 illus.online resource. :
Contained By:
Springer Nature eBook
標題:
Computational intelligence. -
電子資源:
https://doi.org/10.1007/978-3-319-73214-5
ISBN:
9783319732145
Optimized Cloud Based Scheduling
Tan, Rong Kun Jason.
Optimized Cloud Based Scheduling
[electronic resource] /by Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu. - 1st ed. 2018. - XIII, 99 p. 33 illus.online resource. - Data, Semantics and Cloud Computing,7592524-6593 ;. - Data, Semantics and Cloud Computing,759.
Introduction -- Background -- Benchmarking -- Computation of Large Datasets -- Optimized Online Scheduling Algorithms.
This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.
ISBN: 9783319732145
Standard No.: 10.1007/978-3-319-73214-5doiSubjects--Topical Terms:
568984
Computational intelligence.
LC Class. No.: Q342
Dewey Class. No.: 006.3
Optimized Cloud Based Scheduling
LDR
:02509nam a22004095i 4500
001
988031
003
DE-He213
005
20200702144739.0
007
cr nn 008mamaa
008
201225s2018 gw | s |||| 0|eng d
020
$a
9783319732145
$9
978-3-319-73214-5
024
7
$a
10.1007/978-3-319-73214-5
$2
doi
035
$a
978-3-319-73214-5
050
4
$a
Q342
072
7
$a
UYQ
$2
bicssc
072
7
$a
TEC009000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
006.3
$2
23
100
1
$a
Tan, Rong Kun Jason.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1280375
245
1 0
$a
Optimized Cloud Based Scheduling
$h
[electronic resource] /
$c
by Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu.
250
$a
1st ed. 2018.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2018.
300
$a
XIII, 99 p. 33 illus.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Data, Semantics and Cloud Computing,
$x
2524-6593 ;
$v
759
505
0
$a
Introduction -- Background -- Benchmarking -- Computation of Large Datasets -- Optimized Online Scheduling Algorithms.
520
$a
This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.
650
0
$a
Computational intelligence.
$3
568984
650
0
$a
Artificial intelligence.
$3
559380
650
0
$a
Application software.
$3
528147
650
1 4
$a
Computational Intelligence.
$3
768837
650
2 4
$a
Artificial Intelligence.
$3
646849
650
2 4
$a
Information Systems Applications (incl. Internet).
$3
881699
700
1
$a
Leong, John A.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1280376
700
1
$a
Sidhu, Amandeep S.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1074787
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783319732121
776
0 8
$i
Printed edition:
$z
9783319732138
776
0 8
$i
Printed edition:
$z
9783030103330
830
0
$a
Data, Semantics and Cloud Computing,
$x
2524-6593 ;
$v
759
$3
1280377
856
4 0
$u
https://doi.org/10.1007/978-3-319-73214-5
912
$a
ZDB-2-ENG
912
$a
ZDB-2-SXE
950
$a
Engineering (SpringerNature-11647)
950
$a
Engineering (R0) (SpringerNature-43712)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入