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  • 1
    Electronic Resource
    Electronic Resource
    Large deviations analysis of the generalized processor sharing policy (1999)
    Springer
    Queueing systems 32 (1999), S. 319-349 
    Details
    ISSN: 1572-9443
    Keywords: large deviations ; communication networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper we consider a stochastic server (modeling a multiclass communication switch) fed by a set of parallel buffers. The dynamics of the system evolve in discrete-time and the generalized processor sharing (GPS) scheduling policy of [25] is implemented. The arrival process in each buffer is an arbitrary, and possibly autocorrelated, stochastic process. We obtain a large deviations asymptotic for the buffer overflow probability at each buffer. In the standard large deviations methodology, we provide a lower and a matching (up to first degree in the exponent) upper bound on the buffer overflow probabilities. We view the problem of finding a most likely sample path that leads to an overflow as an optimal control problem. Using ideas from convex optimization we analytically solve the control problem to obtain both the asymptotic exponent of the overflow probability and a characterization of most likely modes of overflow. These results have important implications for traffic management of high-speed networks. They extend the deterministic, worst-case analysis of [25] to the case where a detailed statistical model of the input traffic is available and can be used as a basis for an admission control mechanism.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019151423773
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Feature-based methods for large scale dynamic programming (1996)
    Springer
    Machine learning 22 (1996), S. 59-94 
    Details
    ISSN: 0885-6125
    Keywords: Compact representation ; curse of dimensionality ; dynamic programming ; features ; function approximation ; neuro-dynamic programming ; reinforcement learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We develop a methodological framework and present a few different ways in which dynamic programming and compact representations can be combined to solve large scale stochastic control problems. In particular, we develop algorithms that employ two types of feature-based compact representations; that is, representations that involve feature extraction and a relatively simple approximation architecture. We prove the convergence of these algorithms and provide bounds on the approximation error. As an example, one of these algorithms is used to generate a strategy for the game of Tetris. Furthermore, we provide a counter-example illustrating the difficulties of integrating compact representations with dynamic programming, which exemplifies the shortcomings of certain simple approaches.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00114724
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
  • 3
    Electronic Resource
    Electronic Resource
    Feature-Based Methods for Large Scale Dynamic Programming (1996)
    Springer
    Machine learning 22 (1996), S. 59-94 
    Details
    ISSN: 0885-6125
    Keywords: Compact representation ; curse of dimensionality ; dynamic programming ; features ; function approximation ; neuro-dynamic programming ; reinforcement learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We develop a methodological framework and present a few different ways in which dynamic programming and compact representations can be combined to solve large scale stochastic control problems. In particular, we develop algorithms that employ two types of feature-based compact representations; that is, representations that involve feature extraction and a relatively simple approximation architecture. We prove the convergence of these algorithms and provide bounds on the approximation error. As an example, one of these algorithms is used to generate a strategy for the game of Tetris. Furthermore, we provide a counter-example illustrating the difficulties of integrating compact representations with dynamic programming, which exemplifies the shortcomings of certain simple approaches.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1018008221616
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
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