Industrial Networks and Intelligent Systems. Second International Conference, INISCOM 2016, Leicester, UK, October 31 – November 1, 2016, Revised Selected Papers

Research Article

Recovering Request Patterns to a CPU Processor from Observed CPU Consumption Data

  • @INPROCEEDINGS{10.1007/978-3-319-52569-3_2,
        author={Hugo Hammer and Anis Yazidi and Alfred Bratterud and H\ae{}rek Haugerud and Boning Feng},
        title={Recovering Request Patterns to a CPU Processor from Observed CPU Consumption Data},
        proceedings={Industrial Networks and Intelligent Systems. Second International Conference, INISCOM 2016, Leicester, UK, October 31 -- November 1, 2016, Revised Selected Papers},
        proceedings_a={INISCOM},
        year={2017},
        month={6},
        keywords={Classification Co-occurrence information Text mining Tweets},
        doi={10.1007/978-3-319-52569-3_2}
    }
    
  • Hugo Hammer
    Anis Yazidi
    Alfred Bratterud
    Hårek Haugerud
    Boning Feng
    Year: 2017
    Recovering Request Patterns to a CPU Processor from Observed CPU Consumption Data
    INISCOM
    Springer
    DOI: 10.1007/978-3-319-52569-3_2
Hugo Hammer1,*, Anis Yazidi1,*, Alfred Bratterud1,*, Hårek Haugerud1,*, Boning Feng1,*
  • 1: Oslo and Akershus University College of Applied Sciences
*Contact email: hugo.hammer@hioa.no, anis.yazidi@hioa.no, alfred.bratterud@hioa.no, harek.haugerud@hioa.no, boning.feng@hioa.no

Abstract

Statistical queuing models are popular to analyze a computer systems ability to process different types requests. A common strategy is to run stress tests by sending artificial requests to the system. The rate and sizes of the requests are varied to investigate the impact on the computer system. A challenge with such an approach is that we do not know if the artificial requests processes are realistic when the system are applied in a real setting. Motivated by this challenge, we develop a method to estimate the properties of the underlying request processes to the computer system when the system is used in a real setting. In particular we look at the problem of recovering the request patterns to a CPU processor. It turns out that this is a challenging statistical estimation problem since we do not observe the request process (rate and size of the requests) to the CPU directly, but only the CPU usage in disjoint time intervals.