H. Heffes and D. M. Lucantoni, "A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance," IEEE JSAC,vol. SAC-4, no. 6, pp. 856--868, Sep. 1986 Home/Search Document Not in Database Summary Related Articles Check |
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Circulant Preconditioners for Stochastic Automata Networks - Chan, Ching (Correct)
....to illustrate the fast convergence of our method. Key Words: Stochastic Automata Networks, Circulant Preconditioners, Preconditioned Conjugate Gradient Methods. 1 Introduction Stochastic Automata Networks (SANs) are widely used in modeling queueing systems [5, 6, 7, 30] communication systems [29, 32, 40], manufacturing systems and inventory control [8] and also computer networks [34] The SAN approach has a more compact and efficient representation when compared to the stochastic Petri nets approach [1, 34] Moreover because of the special structure of the resulting representations, matrix vector ....
H. Heffes and D. Lucantoni, A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance, IEEE J. Select. Areas Commun., SAC-4 (1986), pp. 856--868. 18
Circulant Preconditioners for Markov Modulated Poisson.. - Ching, Chan, Zhou (1996) (Correct)
....Squared Method, Manufacturing Systems, Hedging Point Policy. Subject Classifications. 65C20, 65F10. 1 Introduction The Markov modulated Poisson process (MMPP) is a generalization of the Poisson process and is widely used as the input model of communication systems such as data traffic systems [8] and ATM networks [23] An MMPP is a Poisson process whose instantaneous rate is itself a stationary random process which varies according to an irreducible n state Markov chain. If n is 1, then the process is just a Poisson process. We say that the MMPP is in phase k, 1 k n, when the ....
....3: The Optimal (h ) for Different i and . 8 Concluding Remarks In this paper, we propose a fast algorithm for solving the steady state probability distribution for queueing systems with MMPP inputs. The MMPP is commonly used in modeling the inputs of many physical systems, see Heffes [8] and Hellstern [13] for instance. Here we relate the MMPP to the production process of unreliable manufacturing systems under the hedging point production control. Our algorithm derived for the queueing systems can be applied to obtain the optimal hedging point. Numerical examples are reported to ....
H. Heffes and D. Lucantoni, A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance, IEEE J. Select. Areas Commun., SAC-4 (1986), pp. 856--868.
An Analysis of the Phase Transition Phenomenon in Packet Networks - Mandies, Kim (2000) (Correct)
....7) O b vq, O. 0, 4.3 Numerical results We will conclude this paper by the calculation of the decay rate for a specific scenario of a voice multiplexer. This example is taken from the study of Sriram and Whitt [25] and is frequently used as a benchmark to compare computation methods [10, 19, 20]. The model consists of a 1.536 Mbit s multiplexer. Voice sources are coded at a rate of 32 kbit s; silences are detected and suppressed. The resulting trafllc stream consists of activities of mean length 352 ms, and silences of mean length 650 ms. We multiplex 80 of these sources on a link of ....
H. HErrES AND D. LUCANTONI. A Markov modulated characterization of packetized voice and data trac and related statistical multiplexer performance. IEEE Journal on Selected Areas in Communications, 4:856 - 868, 1986.
Characterizing the Variability of Arrival Processes with Indices.. - Gusella (1990) (33 citations) (Correct)
....used in their estimation. In the next three subsections, we will calculate the IDI for the class of renewal models with hyperexponential interarrival times, the IDC for batch Poisson processes, and present Heffes and Lucantoni s derivation of the IDC for Markov modulated Poisson processes [9]. C. IDI for Processes with Hyperexponential Interarrival Time Distributions The hyperexponential distribution of order k, H k , is the weighted sum (mixture) of k exponential distributions: F H k (t) Pr(H k t ) a i (1 e l t with weights a i 0, satisfying a i = 1, and ....
....we obtain for the two moments 1 = and 2 = l 1 s 2 l 2 s 1 ) l 1 s 2 l 2 s 1 l 1 l 2 ) 2(l 1 s 1 l 2 s 2 s 1 2s 1 s 2 s 2 ############################## from which we can easily derive the squared coefficient of variation for intervals: C J 1. Heffes and Lucantoni [9] derive a formula for the IDC of a 2 state MMPP process: I t = 1 (l 1 s 2 l 2 s 1 )t (1 e (s 1 s 2 )t ) 7) The asymptote of the IDC is = 1 and it is also straightforward to verify that 1 I t 0 rt 0 1 e rt 0 , 8) where r = s 1 s 2 ....
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Heffes, H. and D. M. Lucantoni, A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance, IEEE Journal on Selected Areas in Communications SAC-4, 6 (September 1986), 856-868.
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....on the DiffServ link. I Host 2 Host 3 Host Z Fig. 2. Class B Model The SSQ is modeled as a queue in a Markovian environment [4,10] to reflect stochastic nature of the capacity available to the Class B packets given the effect of Class A CBR connection loading. We use matrix analytic methods [3] to obtain the numerical results of the delay of Class B packets. Class B packet generation model The creation of Class B packets by hosts for transfer across the DiffServ Link is modeled using a two state Markov Modulated Poisson process (MMPP) 3] The MMPP process models the aggregate packet ....
....loading. We use matrix analytic methods [3] to obtain the numerical results of the delay of Class B packets. Class B packet generation model The creation of Class B packets by hosts for transfer across the DiffServ Link is modeled using a two state Markov Modulated Poisson process (MMPP) [3]. The MMPP process models the aggregate packet generation rate from all the hosts. The MMPP process captures the bursty nature of data traffic. A two state MMPP is an altemating Markovian process with two packet generation states, where the generation process in generation state m is a Poisson ....
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H. Heftes and D. M. Lucantoni, "A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance," IEEE JSAC, vol. SAC-4, no. 6, September 1986
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Heffes, H. and Lucantoni, D. M., "A Markov modulated characterization of packetized voice and data traffic and related statistical multiplexer performance," IEEE J. on Sel. Areas in Comm., vol. SAC-4, pp. 856-868, 1986.
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H. Heffes and D. M. Lucantoni, "A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance," IEEE JSAC,vol. SAC-4, no. 6, pp. 856--868, Sep. 1986
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H. Heffes and D. M. Lucantoni, "A Markov modulated characterization of packetized voice and data traffic and related statistical multiplexer performance," IEEE J. Select. Areas Commun., vol. SAC-4, pp. 856--868, Sept. 1986.
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H. Heffes and D. M. Lucantoni, "A Markov modulated characterization of packetized voice and data traffic and related statistical performance, " IEEE J. Select. Areas Commun., vol. 4, pp. 856-868, 1986.
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H. Heffes and D. Lucantoni, "A Markov modulated characterization of packetized voice and data traffic and related statistical multiplexer performance," IEEE J. Select. Areas Commun., vol. SAC-4, pp. 856--868, Sept. 1986.
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H. Heffes and D. M. Lucantoni, "A markov modulated characterization of packetized voice and data traffic and related statistical multiplexer performance," IEEE Journal on Selected Areas in Comm., vol. 4, pp. 856--868, September 1986.
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H. Heffes and D. M. Lucantoni, "A markov modulated characterization of packetized voice and data traffic and related statistical multiplexer performance," IEEE Journal on Selected Areas in Communications, vol. 4, September 1986.
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Heffes, H. and Lucantoni, D. M. (1986). A markov modulated characterization of packetized voice and data traffic and related statistical multiplexer performance. IEEE J. Select. Areas Commun., 4(6):856--868.
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H. Heffes and D. M. Lucantoni. "A Markov-modulated characterization of packetized voice and data traffic and related statistical multiplexer performance". IEEE J. on Selected Areas in Commun., 4(6):856--868, 1986.
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H. Heffes and D.M. Lucantoni, "A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance," IEEE Journal on Selected Areas in Communications, Vol. SAC-4, No. 6, pp. 856-868, Sept. 1986.
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H. Heffes and D. M. Lucantoni, "A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance," IEEE Journal on Selected Areas in Communications, vol. SAC-4, no. 6, pp. 856868, September 1986.
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H. Heffes and D. M. Lucantoni, "A markov modulated characterization of packetized voice and data traffic and related statistical multiplexer performance," IEEE Journal on Selected Areas in Communications, vol. 4, September 1986.
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H. Heffes and D. M. Lucantoni. A markov modulated characterization of packetized voice and data traffic and related statistical multiplexer performance. IEEE Journal on Selected Areas in Communications, 4:856-- 868, 1986.
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