A storage model for data communication systems

We consider a storage model where the input and demand are modulated by an underlying Markov chain. Such models arise in data communication systems. The input is a Markov-compound Poisson process and the demand is a Markov linear process. The demand is satisfied if physically possible. We study the properties of the demand and its inverse, which may be viewed as transformed time clocks. We show that the unsatisfied demand is related to the infimum of the net input and that, under suitable conditions, it is an additive functional of the input process. The study of the storage level is based on a detailed analysis of the busy period, using techniques based on infinitesimal generators. The transform of the busy period is the unique solution of a certain matrix-functional equation. Steady state results are also obtained; these are not obvious generalizations of the results for simple storage models. In particular, a generalization of the Pollaczek-Khinchin formula brings new insight.

[1]  J. Goldstein Semigroups of Linear Operators and Applications , 1985 .

[2]  D. Mitra Stochastic theory of a fluid model of producers and consumers coupled by a buffer , 1988, Advances in Applied Probability.

[3]  D. Mitra,et al.  Stochastic theory of a data-handling system with multiple sources , 1982, The Bell System Technical Journal.

[4]  Yixin Zhu,et al.  Markov-modulated queueing systems , 1989, Queueing Syst. Theory Appl..

[5]  John P. Lehoczky,et al.  Channels that Cooperatively Service a Data Stream and Voice Messages , 1982, IEEE Trans. Commun..

[6]  Ioannis Karatzas,et al.  Brownian Motion and Stochastic Calculus , 1987 .

[7]  N. U. Prabhu,et al.  Markov-modulated Single Server Queueing Systems , 1994 .

[8]  M. Bartlett,et al.  Markov Processes and Potential Theory , 1972, The Mathematical Gazette.

[9]  N. U. Prabhu,et al.  Stochastic Storage Processes , 1980 .

[10]  Erik A. van Doorn,et al.  Conditional PASTA , 1988 .

[11]  A. Friedman Foundations of modern analysis , 1970 .

[12]  Paul H. Zipkin,et al.  Inventory Models with Continuous, Stochastic Demands , 1991 .

[13]  Robert M. Blumenthal Excursions of Markov Processes , 1991 .

[14]  Debasis Mitra,et al.  Analysis and design of rate-based congestion control of high speed networks, I: stochastic fluid models, access regulation , 1991, Queueing Syst. Theory Appl..

[15]  A. A. Jagers,et al.  A Fluid Reservoir Regulated by a Birth-Death Process , 1988 .

[16]  B. A. Sevast'yanov Influence of Storage Bin Capacity on the Average Standstill Time of a Production Line , 1962 .