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.
Contributor(s): Pop, Florin [editor.] | Potop-Butucaru, Maria [editor.] | SpringerLink (Online service).
Material type: BookSeries: Lecture Notes in Computer Science: 8907Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XII, 217 p. 68 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319134642.Subject(s): Computer science | Computer Science | Computer Science, generalAdditional physical formats: Printed edition:: No titleDDC classification: 004 Online resources: Click here to access onlineA 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.
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 models for resource management in the cloud.
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