M. Reiser, S.S. Lavenberg. Mean-value analysis of closed multichain queueing networks. J.ACM, 27(2):312--322, 1980.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....service discipline is work conserving but not nonpreemptive the mean sojourn times are the same as in the FCFS case. The reason therefore are the equations of the mean value analysis for the mean response time which are the same for M=G=1 LCFS PR and M=M=m FCFS queues (see Reiser and Lavenberg [15]) Following Kobayashi [12 and Allen [1] the mean sojourn time of the M=M=1= N FCFS system is E[ TTR] 1 ; Pi 0 ) 3.6) Finally,wederive the variance of sojourn times by taking the generating function of the distribution of the number of service preemptions (which is of course the ....

Martin Reiser and SteveS.Lavenberg. Mean-value analysis of closed multichain queueing networks. Journal of the Association for Computing Machinery,27( April 1980.

....of the number of customers at a station, the customer throughput at a station, or the waiting time. The success of queueing networks stems mainly from the fact that for the class of product form networks [5] very efficient analysis algorithms, such as Buzen s algorithm [24] or mean value analysis [86], are known, and that software tools for the specification and analysis of QN models were available at In this paper, we do not consider the line of research on non Markovian models such as described, for example, in [47] an early stage [89, 100] Although QN have been extended in various ....

M. Reiser and S. Lavenberg. Mean Value Analysis of Closed Multichain Queueing Networks. Journal of the ACM, 27(2):313--322, 1980.

....[11] Second, the algorithm s time and space requirements increase exponentially with K; more specifically, they are proportional to [ Nk l) Hence, the algorithm is not applicable to networks with more than a few chains. The mean value analysis (MVA) algorithm of Reiser and Lavenberg [23] by passes the evaluation of G( and computes the performance measures of mean queue lengths and chain throughputs directly. It avoids the problem of floating point overflows (Floating point underflows may still occur [20] However, its time and space requirements also grow exponentialiv ....

Reiser M. and So S. Lavenberg, "Mean Value Analysis of Closed Multi-chain Queueing Networks," JACM, April 1980, pp. 313-322

....information is available. In the following, we compare probabilistic analysis to serialization analysis in the MRM case (for N 1) in order to demonstrate the general validity of the lower bound approach. Since the MRM maps to a separable queuing network [7] Mean Value Analysis (MVA) [29] may be applied , which yields = N(r l ) where the response time r of the server is given by the MVA recursion r (0) 0 ; r (n 1) 1 Pr l r Figure 1 relates the lower and upper bound from serialization analysis (SA) to the probabilistic prediction T . The ....

M. Reiser and S.S. Lavenberg, "Mean value analysis of closed multichain queueing networks," Journal of the ACM, vol. 27, Apr. 1980, pp. 313--322.

....the time a processor (job server) is busy between memory accesses is not constant, and has been approximated by a negative exponential distribution. A very complicated expression requiring Laplace transformations has been derived in the past [5] We used the well known Mean Value Analysis (M.V.A. [6] to solve this specific M.R.M. The queuing model used, is shown in figure 1. We assume that a processor is busy with one job at the time. There are N parallel operating processors, represented by N servers without a queue. Any processor executes instructions, of which the time durations Z have a ....

M. Reiser and S.S. Laveberg, "Mean-value Analysis of Closed Multichain Queueing Networks," Journal of the ACM, 27(2), 1980, pp. 313--322.

....[11] Second, the atgorithm s time and space requirements increase exponentially with K; more specifically, they are proportional to H (Nk l) Hence, the algorithm is not applicable to networks with more than a few chains. The mean value analysis (MVA) algorithm of Reiser and Lavenberg [23] by passes the evaluation of G( and computes the performance measures of mean queue lengths and chain throughputs directly. It avoids the problem of floating point overflows (Floating point underflows may still occur [20] However, its time and space requirements also grow exponentially ....

Reiser, M. and S.S. Lavenberg, "Mean Value Analysis of Closed Multi-chain Queueing Networks," JACM, April 1980, pp. 313-322.

.... networks and other complex systems [5,14,19,28,38] The success of these models has largely been due to the excellent algorithms for computing the steady state performance measures that have been developed, such as the convolution algorithm [6,35] the mean value analysis (MVA) algorithm [36], the tree convolution algorithm [27] the recursion by chain algorithm (RECAL) 12,13] the mean value analysis by chain (MVAC) algorithm [15] and the distribution analysis by chain (DAC) algorithm [37] see [5,14,28] for an overview. While these algorithms for closed queueing networks have ....

....so nice as the subclass we consider here, but they tend to be tractable. See [7] for generating functions of other product form models. Generating functions of normalization constants have not been used much to study closed queueing networks, but they have been used. Indeed, Reiser and Kobayashi [36] used generating functions to derive their convolution algorithm for the normalization constants in multi chain networks. Another early use of generating functions is by Williams and Bhandiwad [39] Kelly [19] also briefly discusses generating functions. More recently, in the tradition of the ....

REISER, M. and LAVENBERG, S. S. Mean value analysis of closed multichain queueing networks. J. ACM 27 (1980) 313-322.

....with similar cache behavior across invocations and few, if any, branches. Thus, for many applications the service time distributions for handlers will be much closer to a constant distribution. This section discusses how to extend the model with an approximation, due to Reiser and Lavenberg [27], to account for arbitrary handler service time distributions, with squared coefficient of variation given by 2 9 . For most systems it will be appropriate to assume either 2 9 0 or 2 9 1. When a message arrives at a given node, there is a probability that it will find a ....

M. Reiser and S.S. Lavenberg. Mean Value Analysis of Closed Multichain Queueing Networks. Report RC-7023, IBM T.J. Watson Research Center, March 1978.

.... and popular technique (see e.g. 9] for an introduction to the analysis of computer systems using queueing network models) In this paper, we consider analysis of single class closed queueing network models (QNM) Besides exact solution techniques such as the mean value analysis (MVA) algorithm [15] that provides a single mean value for performance measures, analysis of system bottlenecks (BN) is a popular technique especially for the capacity planning of large systems [14, 16] It requires only very little computation and is thus often preferred as a first cut modeling tool. However, ....

Reiser, M. and Lavenberg, S.S., Mean-Value Analysis of Closed Multichain Queueing Networks. Journal of the ACM, 27 (1980), 313--322.

....devices K, number of jobs N , the number of intervals m per parameter histogram, and the number of customer classes C) on the time complexity of the algorithms. For multiclass systems we consider the time complexity of a popular solution technique, the Exact Multiple Class MVA (EMC MVA) algorithm [15]. Consider a QNM consisting of K devices and N jobs. Let the service demands be modelled by histograms each of which consists of m intervals. Stressing the analogy between histogram intervals in our approach and classes in the EMC MVA, we compare this QNM to a multiple class QNM with C classes and ....

M. Reiser and S. S. Lavenberg. Mean-Value Analysis of Closed Multichain Queueing Networks. Journal of the Association for Computing Machinery, 27(2):313--322, April 1980.

....N) and G(M, N 1) Other performance metrics, such as mean queue lengths, centre utilisations, etc. are found using G. Although very efficient, the convolution algorithm is not very intuitive, and its computations can be affected by overflow or underflow for large networks. Reiser and Lavenberg [5] developed Parallelising the Mean Value Analysis Algorithm Claudio Gennaro and Peter J.B. King Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milano, Italy; E mail: gennaro elet.polimi.it; Department of Computing and Electrical Engineering, Heriot Watt University, ....

Reiser, M., Lavenberg, S.S. "Mean Value Analysis of Closed Multichain Queueing Networks." Journal of the ACM,Vol. 27, No. 2, pp 313-322, 1980.

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M. Reiser, S.S. Lavenberg. Mean-value analysis of closed multichain queueing networks. J.ACM, 27(2):312--322, 1980.

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M. Reiser and S. Lavenberg. Mean-value analysis of closed multichain queueing networks. JACM, 27(2):312-- 322, 1980.

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Reiser, M., & Lavenberg, S.S. (1980). Mean-value analysis of closed multichain queueing networks. Journal of the ACM, 27(2),312--322.

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M. Reiser and S.S. Lavenberg. Mean-value analysis of closed multichain queueing networks. J.ACM, 27(2):312--322, 1980.

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Reiser, M., & Lavenberg, S.S. (1980). Mean-value analysis of closed multichain queueing networks. Journal of the ACM, 27(2),312--322.

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M. Reiser and S. S. Lavenberg. Mean value analysis of closed multichain queueing networks. Journal of the ACM, 27: 313--322, 1980.

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Reiser, M., Lavenberg, S.S. "Mean-value analysis of closed multichain queueing networks", Journal of the ACM, 27, 2, April 1980, pp. 313 -- 322.

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M. Reiser and S. S. Lavenberg. Mean-value analysis of closed multichain queueing networks. Journal of the Association for Computing Machinery, 27(2):313-- 322, 1980.

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Reiser, M., Lavenberg, S. S. "Mean-value analysis of closed multichain queueing networks", Journal of the ACM, 27, 2, April 1980, pp. 313 -- 322.

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M. Reiser, Mean Value Analysis of Closed Multichain Queueing Networks, IBM Research Report RC 70 23, Yorktown Heights, N.Y., 1978.

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M. Reiser and S. Lavenberg. Mean Value Analysis of Closed Multichain Queueing Networks. Journal of the ACM, 27(2):313--322, 1980.

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M. Reiser and S. S. Lavenberg. Mean value analysis of closed multichain queueing networks. Journal of the ACM, 27(2):313322, April 1980.

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REISER, M. and LAVENBERG, S. S. Mean value analysis of closed multichain queueing networks. J. ACM 27 (1980) 313-322.

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Martin Reiser and Stephen Lavenberg. 1980. Mean value analysis of closed multichain queueing networks. Journal of the ACM 27: 313-322.

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