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            Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

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            Contributor(s)
            Indrusiak, Leando Soares (editor)
            Dziurzanski, Piotr (editor)
            Kumar Singh, Amit (editor)
            Language
            English
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            Abstract
            The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/181550
            Keywords
            Computer architecture and logic design; Energy; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYF Computer architecture and logic design; thema EDItEUR::P Mathematics and Science::PH Physics::PHD Classical mechanics::PHDY Energy
            DOI
            10.1201/9781003337997
            ISBN
            9781000794380, 9788793519084, 9781003337997
            Publisher
            Taylor & Francis
            Publisher website
            http://www.taylorandfrancis.com/
            Publication date and place
            2016
            Imprint
            River Publishers
            Pages
            178
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            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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