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  • numerical analysis  (1)
  • randomization  (1)
  • Springer  (2)
  • 1
    Electronic Resource
    Electronic Resource
    A class of hierarchical queueing networks and their analysis (1994)
    Springer
    Queueing systems 15 (1994), S. 59-80 
    Details
    ISSN: 1572-9443
    Keywords: Hierarchical queueing networks ; Markov chains ; numerical analysis ; steady state solution ; transient solution
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Queueing networks are an adequate model type for the analysis of complex system behavior. Most of the more realistic models are rather complex and do not fall into the easy solvable class of product form networks. Those models have to be analyzed by numerical solution of the underlying Markov chain and/or approximation techniques including simulation. In this paper a class of hierarchically structured queueing networks is considered and it is shown that the hierarchical model structure is directly reflected in the state space and the generator matrix of the underlying Markov chain. Iterative solution techniques for stationary and transient analysis can be modified to make use of the model structure and allow an efficient numerical analysis of large, up to now not solvable queueing networks.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01189232
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    A hybrid analysis approach for finite-capacity queues with general inputs and phase type service (2000)
    Springer
    Queueing systems 35 (2000), S. 167-183 
    Details
    ISSN: 1572-9443
    Keywords: discrete event simulation ; randomization ; finite capacity queues ; phase type distributions ; general arrival stream ; stationary analysis ; transient analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A new analysis method for queueing systems with general input stream and phase type service time distributions is introduced. The approach combines discrete event simulation and numerical analysis of continuous time Markov chains. Simulation is used to represent the arrival process, whereas the service process is analyzed with numerical techniques. In this way the state of the system is characterized by a probability vector rather than by a single state. The use of a distribution vector reduces the variance of result estimators such that the width of confidence intervals is often reduced compared to discrete event simulation. This, in particular, holds for measures based on rare events or states with a small probability. The analysis approach can be applied for a wide variety of result measures including stationary, transient and accumulated measures.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019194027833
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    Paper (German National Licenses)
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