• DocumentCode
    3001381
  • Title

    Optimal Partitioning of a Multicore Server Processor

  • Author

    Li, Keqin

  • Author_Institution
    Dept. of Comput. Sci., State Univ. of New York, New Paltz, NY, USA
  • fYear
    2012
  • fDate
    21-25 May 2012
  • Firstpage
    1803
  • Lastpage
    1811
  • Abstract
    Optimal partitioning of a multicore server processor in a cloud computing environment, i.e., optimal system (virtual server) configuration for some given types of applications is considered in this paper. Such optimization is important for dynamic resource provision and on-demand server customization in a cloud computing environment for certain specific types of applications, such that the overall system performance is optimized. A multicore server processor is treated as a group of queueing systems with multiple servers, i.e., M/M/m queueing systems. The system performance measure is the average task response time. The problem of optimal multicore server processor partitioning is formulated and solved. The above problem is a well defined optimization problem. We show that although the problem is sophisticated, it can be solve by a numerical algorithm. Numerical data are demonstrated for the problem.
  • Keywords
    cloud computing; file servers; multiprocessing systems; optimisation; queueing theory; virtual machines; M/M/m queueing system; cloud computing environment; dynamic resource provision; multicore server processor; numerical algorithm; on-demand server customization; optimal partitioning; optimal system; optimization problem; system performance measure; task response time; virtual server configuration; Cloud computing; Manganese; Multicore processing; Optimization; Servers; System performance; Time factors; Multicore server processor; processor partition; queueing model; response time;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4673-0974-5
  • Type

    conf

  • DOI
    10.1109/IPDPSW.2012.223
  • Filename
    6270857