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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://library.oapen.org/handle/20.500.12657/59730
    Keywords
    Computer architecture and logic design; Energy
    DOI
    10.1201/9781003337997
    ISBN
    9781000794380, 9788793519084, 9781003337997, 9781000794380
    Publisher
    Taylor & Francis
    Publisher website
    https://taylorandfrancis.com/
    Publication date and place
    2016
    Imprint
    River Publishers
    Classification
    Computer architecture & logic design
    Energy
    Pages
    178
    Rights
    http://creativecommons.org/licenses/by-nc/4.0
    • Imported or submitted locally

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    Credits

    • logo Scoss
    • logo EU
    • logo Scoss
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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