# 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. Contenuti 1 Necessary conditions for a Jackson network 2 Teorema 3 Definizione 3.1 Teorema 3.2 Esempio 4 Generalized Jackson network 4.1 Brownian approximation 5 Guarda anche 6 References Necessary conditions for a Jackson network A network of m interconnected queues is known as a Jackson network or Jacksonian network 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 {stile di visualizzazione P_{ij}} or leave the system with probability {displaystyle 1-sum _{j=1}^{m}P_{ij}} , quale, 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 {displaystyle rho _{io}} is less than 1 at every queue, the equilibrium state probability distribution exists and for state {displaystyle scriptstyle {(K_{1},K_{2},ldot ,K_{m})}} is given by the product of the individual queue equilibrium distributions {stile di visualizzazione pi (K_{1},K_{2},ldot ,K_{m})=prodotto _{io=1}^{m}pi_{io}(K_{io})=prodotto _{io=1}^{m}[ro _{io}^{K_{io}}(1-ro _{io})].} The result {stile di visualizzazione pi (K_{1},K_{2},ldot ,K_{m})=prodotto _{io=1}^{m}pi_{io}(K_{io})} also holds for M/M/c model stations with ci servers at the {displaystyle i^{testo{th}}} station, with utilization requirement {displaystyle rho _{io}0} . Each arrival is independently routed to node j with probability {stile di visualizzazione p_{0j}geq 0} e {somma dello stile di visualizzazione _{j=1}^{J}p_{0j}=1} . Upon service completion at node i, a job may go to another node j with probability {stile di visualizzazione p_{ij}} or leave the network with probability {stile di visualizzazione p_{i0}=1-somma _{j=1}^{J}p_{ij}} .

Hence we have the overall arrival rate to node i, {displaystyle lambda _{io}} , including both external arrivals and internal transitions: {displaystyle lambda _{io}=alpha p_{0io}+somma _{j=1}^{J}lambda _{j}p_{ji},i=1,ldot ,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 {displaystyle lui _{j}} in the above.) Definire {displaystyle a=(alpha p_{0io})_{io=1}^{J}} , then we can solve {displaystyle lambda =(I-P^{T})^{-1}un} .

All jobs leave each node also following Poisson process, e definire {displaystyle lui _{io}(X_{io})} as the service rate of node i when there are {stile di visualizzazione x_{io}} jobs at node i.

Permettere {stile di visualizzazione X_{io}(t)} denote the number of jobs at node i at time t, e {displaystyle mathbf {X} =(X_{io})_{io=1}^{J}} . Then the equilibrium distribution of {displaystyle mathbf {X} } , {stile di visualizzazione pi (mathbf {X} )=P(mathbf {X} =mathbf {X} )} is determined by the following system of balance equations: {stile di visualizzazione {inizio{allineato}&pi (mathbf {X} )somma _{io=1}^{J}[alpha p_{0io}+in _{io}(X_{io})(1-p_{ii})]\={}&sum _{io=1}^{J}[pi (mathbf {X} -mathbf {e} _{io})alpha p_{0io}+pi (mathbf {X} +mathbf {e} _{io})in _{io}(X_{io}+1)p_{i0}]+somma _{io=1}^{J}somma _{jneq i}pi (mathbf {X} +mathbf {e} _{io}-mathbf {e} _{j})in _{io}(X_{io}+1)p_{ij}.qquad (2)fine{allineato}}} dove {displaystyle mathbf {e} _{io}} denote the {displaystyle i^{testo{th}}} unit vector.

Theorem Suppose a vector of independent random variables {stile di visualizzazione (Y_{1},ldot ,Y_{J})} with each {stile di visualizzazione Y_{io}} having a probability mass function as {stile di visualizzazione P(Y_{io}=n)= p(Y_{io}=0)cdot {frac {lambda _{io}^{n}}{M_{io}(n)}},quad (3)} dove {stile di visualizzazione M_{io}(n)=prodotto _{j=1}^{n}in _{io}(j)} . Se {somma dello stile di visualizzazione _{n=1}^{infty }{frac {lambda _{io}^{n}}{M_{io}(n)}}0} Then by the theorem, we can calculate: {displaystyle lambda =(I-P^{T})^{-1}a={inizio{bmatrice}1&0&0\-0.5&1&0\-0.5&0&1end{bmatrice}}^{-1}{inizio{bmatrice}0.5times 5\0.5times 5\0end{bmatrice}}={inizio{bmatrice}1&0&0\0.5&1&0\0.5&0&1end{bmatrice}}{inizio{bmatrice}2.5\2.5\0fine{bmatrice}}={inizio{bmatrice}2.5\3.75\1.25fine{bmatrice}}} According to the definition of {displaystyle mathbf {Y} } , noi abbiamo: {stile di visualizzazione P(Y_{1}=0)= sinistra(somma _{n=0}^{infty }sinistra({frac {2.5}{15}}Giusto)^{n}Giusto)^{-1}={frac {5}{6}}} {stile di visualizzazione P(Y_{2}=0)= sinistra(somma _{n=0}^{infty }sinistra({frac {3.75}{12}}Giusto)^{n}Giusto)^{-1}={frac {11}{16}}} {stile di visualizzazione P(Y_{3}=0)= sinistra(somma _{n=0}^{infty }sinistra({frac {1.25}{10}}Giusto)^{n}Giusto)^{-1}={frac {7}{8}}} Hence the probability that there is one job at each node is: {stile di visualizzazione 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}}ca 0.00326} Since the service rate here does not depend on state, il {stile di visualizzazione Y_{io}} 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. In generale, this network does not have a product-form stationary distribution, so approximations are sought. Brownian approximation Under some mild conditions the queue-length process[chiarimenti necessari] {stile di visualizzazione Q(t)} of an open generalized Jackson network can be approximated by a reflected Brownian motion defined as {nome dell'operatore dello stile di visualizzazione {RBM} _{Q(0)}(teta ,Gamma ;R).} , dove {stile di visualizzazione theta } is the drift of the process, {stile di visualizzazione Gamma } is the covariance matrix, e {stile di visualizzazione 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^{T})in } {displaystyle Gamma =(gamma _{kell }){testo{ insieme a }}gamma _{kell }=somma _{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 })]+alfa _{K}c_{0,K}^{2}delta _{kell }} {displaystyle R=I-P^{T}} where the symbols are defined as: Definitions of symbols in the approximation formula symbol Meaning {displaystyle alpha =(alfa _{j})_{j=1}^{J}} a J-vector specifying the arrival rates to each node.

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

{stile di visualizzazione P} routing matrix.

{displaystyle lambda _{j}} effective arrival of {displaystyle j^{testo{th}}} node.

{stile di visualizzazione c_{j}} variation of service time at {displaystyle j^{testo{th}}} node.

{stile di visualizzazione c_{0,j}} variation of inter-arrival time at {displaystyle j^{testo{th}}} node.

{displaystyle delta _{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. doi:10.2307/1426753. JSTOR 1426753. ^ Kelly, F. P. (Giugno 1976). "Networks of Queues". Advances in Applied Probability. 8 (2): 416–432. doi:10.2307/1425912. JSTOR 1425912. ^ Jackson, James R. (Dicembre 2004). "Comments on "Jobshop-Like Queueing Systems": The Background". Management Science. 50 (12): 1796–1802. doi:10.1287/mnsc.1040.0268. JSTOR 30046150. ^ Jackson, James R. (Oct 1963). "Jobshop-like Queueing Systems". Management Science. 10 (1): 131–142. doi: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. doi:10.1287/opre.5.4.518. JSTOR 167249. ^ Jackson, James R. (Dicembre 2004). "Jobshop-Like Queueing Systems". Management Science. 50 (12): 1796–1802. doi:10.1287/mnsc.1040.0268. JSTOR 30046149. ^ Reich, Edgar (settembre 1957). "Waiting Times When Queues are in Tandem". Annals of Mathematical Statistics. 28 (3): 768. doi: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. doi: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. doi:10.1093/imaman/6.4.382. ^ Gordon, w. J.; Newell, G. F. (1967). "Closed Queuing Systems with Exponential Servers". Operations Research. 15 (2): 254. doi:10.1287/opre.15.2.254. JSTOR 168557. ^ Goodman, Jonathan B.; Massey, William A. (Dicembre 1984). "The Non-Ergodic Jackson Network". Journal of Applied Probability. 21 (4): 860–869. doi:10.2307/3213702. ^ Walrand, J.; Varaiya, P. (Dicembre 1980). "Sojourn Times and the Overtaking Condition in Jacksonian Networks". Advances in Applied Probability. 12 (4): 1000–1018. doi: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 (unità)Erlang distributionFlow control (data)Message queueNetwork congestionNetwork schedulerPipeline (software)Quality of serviceScheduling (computing)Teletraffic engineering Category Categories: Queueing theory

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