# Jackson network

Jackson networks where a finite population of jobs travel around a closed network also have a product-form solution described by the Gordon–Newell theorem.[10] Contenu 1 Necessary conditions for a Jackson network 2 Théorème 3 Définition 3.1 Théorème 3.2 Exemple 4 Generalized Jackson network 4.1 Brownian approximation 5 Voir également 6 References Necessary conditions for a Jackson network A network of m interconnected queues is known as a Jackson network[11] or Jacksonian network[12] if it meets the following conditions: if the network is open, any external arrivals to node i form a Poisson process, All service times are exponentially distributed and the service discipline at all queues is first-come, first-served, a customer completing service at queue i will either move to some new queue j with probability {style d'affichage P_{ij}} or leave the system with probability {displaystyle 1-sum _{j=1}^{m}P_{ij}} , qui, for an open network, is non-zero for some subset of the queues, the utilization of all of the queues is less than one. Theorem In an open Jackson network of m M/M/1 queues where the utilization {style d'affichage rho _{je}} is less than 1 at every queue, the equilibrium state probability distribution exists and for state {displaystyle scriptstyle {(k_{1},k_{2},ldots ,k_{m})}} is given by the product of the individual queue equilibrium distributions {style d'affichage pi (k_{1},k_{2},ldots ,k_{m})=produit _{je=1}^{m}pi _{je}(k_{je})=produit _{je=1}^{m}[rho _{je}^{k_{je}}(1-rho _{je})].} Le résultat {style d'affichage pi (k_{1},k_{2},ldots ,k_{m})=produit _{je=1}^{m}pi _{je}(k_{je})} also holds for M/M/c model stations with ci servers at the {style d'affichage i^{texte{e}}} station, with utilization requirement {style d'affichage rho _{je}0} . Each arrival is independently routed to node j with probability {style d'affichage p_{0j}gq 0} et {somme de style d'affichage _{j=1}^{J}p_{0j}=1} . Upon service completion at node i, a job may go to another node j with probability {style d'affichage p_{ij}} or leave the network with probability {style d'affichage p_{i0}=1-somme _{j=1}^{J}p_{ij}} .

Hence we have the overall arrival rate to node i, {style d'affichage lambda _{je}} , including both external arrivals and internal transitions: {style d'affichage lambda _{je}=alpha p_{0je}+somme _{j=1}^{J}lambda _{j}p_{ji},i=1,lpoints ,J.qquad (1)} (Since the utilisation at each node is less than 1, and we are looking at the equilibrium distribution i.e. the long-run-average behaviour, the rate of jobs transitioning from j to i is bounded by a fraction of the arrival rate at j and we ignore the service rate {style d'affichage lui _{j}} in the above.) Définir {displaystyle a=(alpha p_{0je})_{je=1}^{J}} , then we can solve {style d'affichage lambda =(I-P^{J})^{-1}un} .

All jobs leave each node also following Poisson process, et définir {style d'affichage lui _{je}(X_{je})} as the service rate of node i when there are {style d'affichage x_{je}} jobs at node i.

Laisser {style d'affichage X_{je}(t)} denote the number of jobs at node i at time t, et {style d'affichage mathbf {X} =(X_{je})_{je=1}^{J}} . Then the equilibrium distribution of {style d'affichage mathbf {X} } , {style d'affichage pi (mathbf {X} )=P(mathbf {X} = mathbf {X} )} is determined by the following system of balance equations: {style d'affichage {commencer{aligné}&pi (mathbf {X} )somme _{je=1}^{J}[alpha p_{0je}+dans _{je}(X_{je})(1-p_{ii})]\={}&sum _{je=1}^{J}[pi (mathbf {X} -mathbf {e} _{je})alpha p_{0je}+pi (mathbf {X} +mathbf {e} _{je})dans _{je}(X_{je}+1)p_{i0}]+somme _{je=1}^{J}somme _{jneq i}pi (mathbf {X} +mathbf {e} _{je}-mathbf {e} _{j})dans _{je}(X_{je}+1)p_{ij}.qquad (2)fin{aligné}}} où {style d'affichage mathbf {e} _{je}} denote the {style d'affichage i^{texte{e}}} unit vector.

Theorem Suppose a vector of independent random variables {style d'affichage (O_{1},ldots ,O_{J})} with each {style d'affichage Y_{je}} having a probability mass function as {style d'affichage P(O_{je}=n)=p(O_{je}=0)cdot {frac {lambda _{je}^{n}}{M_{je}(n)}},quad (3)} où {style d'affichage M_{je}(n)=produit _{j=1}^{n}dans _{je}(j)} . Si {somme de style d'affichage _{n=1}^{infime }{frac {lambda _{je}^{n}}{M_{je}(n)}}0} Then by the theorem, we can calculate: {style d'affichage lambda =(I-P^{J})^{-1}a={commencer{bmatrice}1&0&0\-0.5&1&0\-0.5&0&1end{bmatrice}}^{-1}{commencer{bmatrice}0.5times 5\0.5times 5\0end{bmatrice}}={commencer{bmatrice}1&0&0\0.5&1&0\0.5&0&1end{bmatrice}}{commencer{bmatrice}2.5\2.5\0fin{bmatrice}}={commencer{bmatrice}2.5\3.75\1.25fin{bmatrice}}} According to the definition of {style d'affichage mathbf {Oui} } , Nous avons: {style d'affichage P(O_{1}=0)=gauche(somme _{n=0}^{infime }la gauche({frac {2.5}{15}}droit)^{n}droit)^{-1}={frac {5}{6}}} {style d'affichage P(O_{2}=0)=gauche(somme _{n=0}^{infime }la gauche({frac {3.75}{12}}droit)^{n}droit)^{-1}={frac {11}{16}}} {style d'affichage P(O_{3}=0)=gauche(somme _{n=0}^{infime }la gauche({frac {1.25}{10}}droit)^{n}droit)^{-1}={frac {7}{8}}} Hence the probability that there is one job at each node is: {style d'affichage pi (1,1,1)={frac {5}{6}}cdot {frac {2.5}{15}}cdot {frac {11}{16}}cdot {frac {3.75}{12}}cdot {frac {7}{8}}cdot {frac {1.25}{10}}environ 0.00326} Since the service rate here does not depend on state, la {style d'affichage Y_{je}} s simply follow a geometric distribution.

Generalized Jackson network A generalized Jackson network allows renewal arrival processes that need not be Poisson processes, and independent, identically distributed non-exponential service times. En général, this network does not have a product-form stationary distribution, so approximations are sought.[13] Brownian approximation Under some mild conditions the queue-length process[clarification nécessaire] {style d'affichage Q(t)} of an open generalized Jackson network can be approximated by a reflected Brownian motion defined as {nom de l'opérateur de style d'affichage {RBM} _{Q(0)}(thêta ,Gamma ;R).} , où {thêta de style d'affichage } is the drift of the process, {style d'affichage Gamma } is the covariance matrix, et {style d'affichage R} is the reflection matrix. This is a two-order approximation obtained by relation between general Jackson network with homogeneous fluid network and reflected Brownian motion.

The parameters of the reflected Brownian process is specified as follows: {displaystyle theta =alpha -(I-P^{J})dans } {displaystyle Gamma =(Gamma _{kell }){texte{ avec }}Gamma _{kell }=somme _{j=1}^{J}(lambda _{j}wedge mu _{j})[p_{jk}(delta _{kell }-p_{jell })+c_{j}^{2}(p_{jk}-delta _{jk})(p_{jell }-delta _{jell })]+Alpha _{k}c_{0,k}^{2}delta _{kell }} {displaystyle R=I-P^{J}} where the symbols are defined as: Definitions of symbols in the approximation formula symbol Meaning {displaystyle alpha =(Alpha _{j})_{j=1}^{J}} a J-vector specifying the arrival rates to each node.

{displaystyle mu =(dans )_{j=1}^{J}} a J-vector specifying the service rates of each node.

{style d'affichage P} routing matrix.

{style d'affichage lambda _{j}} effective arrival of {displaystyle j^{texte{e}}} node.

{displaystyle c_{j}} variation of service time at {displaystyle j^{texte{e}}} node.

{displaystyle c_{0,j}} variation of inter-arrival time at {displaystyle j^{texte{e}}} node.

{delta de style d'affichage _{ij}} coefficients to specify correlation between nodes. show See also Gordon–Newell network BCMP network G-network Little's law References ^ Walrand, J; Varaiya, P. (1980). "Sojourn Times and the Overtaking Condition in Jacksonian Networks". Advances in Applied Probability. 12 (4): 1000–1018. est ce que je:10.2307/1426753. JSTOR 1426753. ^ Kelly, F. P. (Juin 1976). "Networks of Queues". Advances in Applied Probability. 8 (2): 416–432. est ce que je:10.2307/1425912. JSTOR 1425912. ^ Jackson, James R.. (Décembre 2004). "Comments on "Jobshop-Like Queueing Systems": The Background". Management Science. 50 (12): 1796–1802. est ce que je:10.1287/mnsc.1040.0268. JSTOR 30046150. ^ Jackson, James R.. (Oct 1963). "Jobshop-like Queueing Systems". Management Science. 10 (1): 131–142. est ce que je:10.1287/mnsc.1040.0268. JSTOR 2627213. A version from January 1963 is available at http://www.dtic.mil/dtic/tr/fulltext/u2/296776.pdf ^ Jackson, J. R. (1957). "Networks of Waiting Lines". Operations Research. 5 (4): 518–521. est ce que je:10.1287/opre.5.4.518. JSTOR 167249. ^ Jackson, James R.. (Décembre 2004). "Jobshop-Like Queueing Systems". Management Science. 50 (12): 1796–1802. est ce que je:10.1287/mnsc.1040.0268. JSTOR 30046149. ^ Reich, Edgar (Septembre 1957). "Waiting Times When Queues are in Tandem". Annales de statistiques mathématiques. 28 (3): 768. est ce que je:10.1214/aoms/1177706889. JSTOR 2237237. ^ Walrand, Jean (Novembre 1983). "A Probabilistic Look at Networks of Quasi-Reversible Queues". IEEE Transactions on Information Theory. 29 (6): 825. est ce que je:10.1109/TIT.1983.1056762. ^ Jackson, R. R. P. (1995). "Book review: Queueing networks and product forms: a systems approach". IMA Journal of Management Mathematics. 6 (4): 382–384. est ce que je:10.1093/imaman/6.4.382. ^ Gordon, O. J; Newell, g. F. (1967). "Closed Queuing Systems with Exponential Servers". Operations Research. 15 (2): 254. est ce que je:10.1287/opre.15.2.254. JSTOR 168557. ^ Goodman, Jonathan B.; Massey, William A. (Décembre 1984). "The Non-Ergodic Jackson Network". Journal of Applied Probability. 21 (4): 860–869. est ce que je:10.2307/3213702. ^ Walrand, J; Varaiya, P. (Décembre 1980). "Sojourn Times and the Overtaking Condition in Jacksonian Networks". Advances in Applied Probability. 12 (4): 1000–1018. est ce que je:10.2307/1426753. ^ Chen, Hong; Yao, David D. (2001). Fundamentals of Queueing Networks: Performance, Asymptotics, and Optimization. Springer. ISBN 0-387-95166-0. hide vte Queueing theory Single queueing nodes D/M/1 queueM/D/1 queueM/D/c queueM/M/1 queue Burke's theoremM/M/c queueM/M/∞ queueM/G/1 queue Pollaczek–Khinchine formulaMatrix analytic methodM/G/k queueG/M/1 queueG/G/1 queue Kingman's formulaLindley equationFork–join queueBulk queue Arrival processes Poisson point processMarkovian arrival processRational arrival process Queueing networks Jackson network Traffic equationsGordon–Newell theorem Mean value analysisBuzen's algorithmKelly networkG-networkBCMP network Service policies FIFOLIFOProcessor sharingRound-robinShortest job nextShortest remaining time Key concepts Continuous-time Markov chainKendall's notationLittle's lawProduct-form solution Balance equationQuasireversibilityFlow-equivalent server methodArrival theoremDecomposition methodBeneš method Limit theorems Fluid limitMean-field theoryHeavy traffic approximation Reflected Brownian motion Extensions Fluid queueLayered queueing networkPolling systemAdversarial queueing networkLoss networkRetrial queue Information systems Data bufferErlang (unité)Erlang distributionFlow control (data)Message queueNetwork congestionNetwork schedulerPipeline (software)Quality of serviceScheduling (computing)Teletraffic engineering Category Categories: Queueing theory

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