000 | 03289nam a22005655i 4500 | ||
---|---|---|---|
001 | 978-3-319-73214-5 | ||
003 | DE-He213 | ||
005 | 20220801214036.0 | ||
007 | cr nn 008mamaa | ||
008 | 180224s2018 sz | s |||| 0|eng d | ||
020 |
_a9783319732145 _9978-3-319-73214-5 |
||
024 | 7 |
_a10.1007/978-3-319-73214-5 _2doi |
|
050 | 4 | _aQ342 | |
072 | 7 |
_aUYQ _2bicssc |
|
072 | 7 |
_aTEC009000 _2bisacsh |
|
072 | 7 |
_aUYQ _2thema |
|
082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aTan, Rong Kun Jason. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _935951 |
|
245 | 1 | 0 |
_aOptimized Cloud Based Scheduling _h[electronic resource] / _cby Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu. |
250 | _a1st ed. 2018. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018. |
|
300 |
_aXIII, 99 p. 33 illus. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
||
347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aData, Semantics and Cloud Computing, _x2524-6607 ; _v759 |
|
505 | 0 | _aIntroduction -- Background -- Benchmarking -- Computation of Large Datasets -- Optimized Online Scheduling Algorithms. | |
520 | _aThis 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 |
_aComputational intelligence. _97716 |
|
650 | 0 |
_aArtificial intelligence. _93407 |
|
650 | 0 |
_aApplication software. _935952 |
|
650 | 1 | 4 |
_aComputational Intelligence. _97716 |
650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
650 | 2 | 4 |
_aComputer and Information Systems Applications. _935953 |
700 | 1 |
_aLeong, John A. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _935954 |
|
700 | 1 |
_aSidhu, Amandeep S. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _935955 |
|
710 | 2 |
_aSpringerLink (Online service) _935956 |
|
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783319732121 |
776 | 0 | 8 |
_iPrinted edition: _z9783319732138 |
776 | 0 | 8 |
_iPrinted edition: _z9783030103330 |
830 | 0 |
_aData, Semantics and Cloud Computing, _x2524-6607 ; _v759 _935957 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-73214-5 |
912 | _aZDB-2-ENG | ||
912 | _aZDB-2-SXE | ||
942 | _cEBK | ||
999 |
_c75892 _d75892 |