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Record Nr. |
UNISA996210519903316 |
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Titolo |
Adaptive Resource Management and Scheduling for Cloud Computing [[electronic resource] ] : First International Workshop, ARMS-CC 2014, held in Conjunction with ACM Symposium on Principles of Distributed Computing, PODC 2014, Paris, France, July 15, 2014, Revised Selected Papers / / edited by Florin Pop, Maria Potop-Butucaru |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
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ISBN |
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Edizione |
[1st ed. 2014.] |
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Descrizione fisica |
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1 online resource (XII, 217 p. 68 illus.) |
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Collana |
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Theoretical Computer Science and General Issues, , 2512-2029 ; ; 8907 |
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Disciplina |
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Soggetti |
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Computer science |
Computer Science |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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A Multi-Capacity Queuing Mechanism in Multi-Dimensional Resource Scheduling -- A Green Scheduling Policy for Cloud Computing -- A Framework for Speculative Scheduling and Device Selection for Task Execution on a Mobile Cloud -- An Interaction Balance Based Approach for Autonomic Performance Management in a Cloud Computing Environment -- Power-efficient Assignment of Virtual Machines to Physical Machines -- Simulation of Multi-Tenant Scalable Cloud-Distributed Enterprise Information Systems -- Towards Type-based Optimizations in Distributed Applications using ABS and JAVA 8 -- A Parallel Genetic Algorithm Framework for Cloud Computing Applications. |
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Sommario/riassunto |
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This book constitutes the thoroughly refereed post-conference proceedings of the First International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, ARMS-CC 2014, held in Conjunction with ACM Symposium on Principles of Distributed Computing, PODC 2014, in Paris, France, in July 2014. The 14 revised full papers (including 2 invited talks) were carefully reviewed and selected from 29 submissions and cover topics such as scheduling methods and algorithms, services and applications, fundamental |
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models for resource management in the cloud. |
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