Gianfranco Ciardo, Raymond A. Marie, Bruno Sericola and Kishor S. Trivedi. Performability analysis using semi-markov reward process. In IEEE Transactions on Computers, volume 39, pages 1251--1264, October 1990.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... of methods in parallel processing [Akyi 92] Model based performance analysis has recognized an overwhelming mass of contributions in the various paradigms: queueing network models [Heid 82, Heid 83, Funk 91, Munt 90] Petri Net modeling [Ajmo 84] and markovian stochastic performance models [Ciar 90, Smit 90b] to give some pointers. Modeling has been applied to describe interdependencies of hardware resources [Mahg 92a] to characterize the behavior of parallel program components and to model the workload for parallel systems [Calz 93] but also approaches integrating parallel program, ....

G. Ciardo, R. A. Marie, B. Sericola, and K. S. Trivedi. "Performability Analysis Using Semi-Markov Reward Processes". IEEE Transactions on Computers, Vol. 39, No. 10, pp. 1251--1264, October 1990.

....i Z 1 0 p i (u) du : 13 Recently, considerable attention has been given to the problem of evaluating the distribution of accumulated reward, Y(x; t) j P [Y (t) x] This is the probability of completing a given amount of useful work, x, within a specific time interval, t. For more details, see [7, 28]. 2.2 Stochastic Timed Petri Nets 2.2.1 Petri nets A Petri net [91, 92] is an abstract, formal model of information flow. It is a powerful method for describing and analyzing the flows of information and the controls in a system. The main use of Petri nets has been in the modeling of systems ....

....given that the system has been operational up to time t, is another. In [64] the conditional expectation is defined to be the expected time to failure given that the failure occurs within a specific time range. All these studies, however, do not consider the MTTF to individual failure states. In [28], it is observed that the probability of absorbing into a partition of absorbing states from a given initial state may be computed as the accumulated reward until absorption by assigning zero reward rate to the states in the partition and positive reward rate to all other absorbing states. The ....

G. Ciardo, R. Marie, B. Sericola, and K. S. Trivedi. Performability analysis using semiMarkov reward processes. IEEE Transactions on Computers, C-39(10):1251--1264, 1990.

....finding the accumulated loss distribution can be formulated as finding the distribution of the accumulated reward of some transient Markov reward process (TMRP) With appropriate transformation of the TMRP, the problem can be reduced to the sojourn time analysis of a transient Markov process. See [23, 24, 25] for details. useful in evaluating the effectiveness of congestion control schemes, e.g. it is essential to be able to estimate specify how long it will take for certain control scheme to bring the system delay (or equivalently the queue size) down to the normal level once a congestion overload ....

G. Ciardo, R.A. Marie, B. Sericola and K. Trivedi, "Performability Analysis using semi-Markov Reward Processes," IEEE Trans. on Comp., Vol. 39, No. 10, 1990, pp. 1251-1264.

....derive an integral expression for performability which they solve numerically. However, the complexity of such an algorithm is exponential in the number of states of the process. Beaudry [5] gives a method for the computation of performability in a Markovian process until absorption. Ciardo et al. [6] generalize Beaudry s approach to a semi Markov reward process and remove the restriction requiring only the absorbing states to be associated with a zero reward rate. Iyer et al. 7] propose an algorithm to compute recursively the moments of the accumulated reward over the mission time, with a ....

G. Ciardo, R. Marie, B. Sericola, and K. S. Trivedi. -- Performability analysis using semiMarkov reward processes. -- IEEE Transactions on Computers, C-39:1251--1264, October 1990.

.... reward processes have the potential to re ect concurrency, contention, faulttolerance, and degradable performance; they can be used to obtain not only program system performance and system reliability availability measures, but also combined measures of performance and reliability availability [3, 9, 27, 29, 42]. Since the Markov process is generated from a concise GSPN model, it is necessary to express the reward structure in terms of GSPN entities. In other words, the GSPN becomes a GSPN reward process which can be automatically transformed into a Markov reward process. Steady state analysis of ....

G. Ciardo, R. A. Marie, B. Sericola, and K. S. Trivedi. Performability analysis using semi-Markov reward processes. IEEE Transactions on Computers. To appear.

....To model the repair of faulty components in repairable systems, cyclic Markov processes are needed. For absorbing Markov processes, Beaudry [8] gave an algorithm to compute the distribution of accumulated reward until system failure when the reward rates are strictly positive and Ciardo et al. [9] extended this method to semi Markov processes for non negative reward rates. For finite mission time and when the reward rates are either 0 or 1, the accumulated reward over the mission time is the interval availability. The distribution of interval availability has been studied in [10] using the ....

G. Ciardo, R. Marie, B. Sericola, and K. S. Trivedi. Performability analysis using semiMarkov reward processes. IEEE Trans. Computers, C-39:1251--1264, October 1990.

....for the computation of the point availability and the expected interval availability using the steady state availability detection can be extended to more general measures such as the point performability and the expected interval performability. In performability modeling (see, for instance, [9, 10, 11, 12, 13, 14, 15, 16] and the references therein) reward rates are associated with states of the model to quantify the ability of the system to perform in the corresponding states. We denote by ae(i) the reward rate associated to the state i 2 S. The reward rates ae(i) are assumed to be nonnegative real numbers. The ....

G. Ciardo, R. Marie, B. Sericola, and K. S. Trivedi, "Performability analysis using semi-Markov reward processes," IEEE Trans. Computers, vol. C-39, pp. 1251--1264, October 1990.

....by process f t g above C starts in state s = s 1 ; s l ) 2 SC 1 is given by vC 1 (s) l X i=1 fi i C ae i ae s i Gamma1 (s i Gamma 1) 1I fs i 0g Y k 6=i ae k ae s k s k : 2. 4) 3 Distribution of the Volume V of Lost Information Using the results of [4, 18], the distribution of random variable V satisfies PrfV tg = ve M t 1; 3.1) where v is given by Proposition 2.1 and matrix M is defined by M = R Gamma1 AB 0 ; 3.2) with reward matrix R being a diagonal matrix over subset B 0 , such that for every k C 1, R(s; s) k Gamma C if s 2 ....

G. Ciardo, R. Marie, B. Sericola, and K.S. Trivedi. Performability analysis using semiMarkov reward processes. IEEE Trans. Computers, 39:1251--1264, October 1990.

....by process f t g above C starts in state s = s 1 ; s l ) 2 SC 1 is given by vC 1 (s) l X i=1 fi i C ae i ae s i Gamma1 (s i Gamma 1) 1I fs i 0g Y k 6=i ae k ae s k s k : 2. 4) 3 Distribution of the Volume V of Lost Information Using the results of [4, 18], the distribution of random variable V satisfies PrfV tg = ve M t 1; 3.1) where v is given by Proposition 2.1 and matrix M is defined by M = R Gamma1 AB 0 ; 3.2) with reward matrix R being a diagonal matrix over subset B 0 , such that for every k C 1, R(s; s) k Gamma C if s 2 ....

G. Ciardo, R. Marie, B. Sericola, and K.S. Trivedi. Performability analysis using semiMarkov reward processes. IEEE Trans. Computers, 39:1251--1264, October 1990.

.... reward processes have the potential to reflect concurrency, contention, faulttolerance, and degradable performance; they can be used to obtain not only program system performance and system reliability availability measures, but also combined measures of performance and reliability availability [3, 9, 27, 29, 42]. Since the Markov process is generated from a concise GSPN model, it is necessary to express the reward structure in terms of GSPN entities. In other words, the GSPN becomes a GSPN reward process which can be automatically transformed into a Markov reward process. Steady state analysis of GSPNs ....

G. Ciardo, R. A. Marie, B. Sericola, and K. S. Trivedi. Performability analysis using semi-Markov reward processes. IEEE Transactions on Computers. To appear.

....integral expression for performability which they solve numerically. However, the complexity of such an algorithm is exponential in the number of states of the process. Beaudry [4] gives a method for the computation of performability in an acyclic Markovian process until absorption. Ciardo et al. [5] generalize Beaudry s approach to a semi Markov reward process and remove the restriction requiring only the absorbing states to be associated with a zero reward rate. Goyal and Tantawi [6] derive a closed form expression (precisely a finite sum of exponential functions) for the performability of ....

G. Ciardo, R. Marie, B. Sericola, and K. S. Trivedi. Performability analysis using semiMarkov reward processes. IEEE Trans. Computers, C-39:1251--1264, October 1990.

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Gianfranco Ciardo, Raymond A. Marie, Bruno Sericola and Kishor S. Trivedi. Performability analysis using semi-markov reward process. In IEEE Transactions on Computers, volume 39, pages 1251--1264, October 1990.

No context found.

G. Ciardo, R.A. Marie, B. Sericola, and K.S. Trivedi. Performability analysis using semi-Markov reward processes. IEEE Transactions on Computers, 39:1252--1264, 1990.

No context found.

Ciardo, G., Marie, R. A., Sericola, B. and Trivedi, K. S. "Performability Analysis Using Semi-Markov Reward Processes", IEEE Trans. On Comput., Vol. 39, NO. 10, pp. 1251-1264, October 1990

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