Optimized Cloud Based Scheduling

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 algori...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Tan, Rong Kun Jason (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Leong, John A. (http://id.loc.gov/vocabulary/relators/aut), Sidhu, Amandeep S. (http://id.loc.gov/vocabulary/relators/aut)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
Έκδοση:1st ed. 2018.
Σειρά:Data, Semantics and Cloud Computing, 759
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Tan, Rong Kun Jason.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
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 
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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. 
650 0 |a Artificial intelligence. 
650 0 |a Application software. 
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650 2 4 |a Information Systems Applications (incl. Internet).  |0 http://scigraph.springernature.com/things/product-market-codes/I18040 
700 1 |a Leong, John A.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
700 1 |a Sidhu, Amandeep S.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
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