Jackson network

Jackson network   (Redirected from Jackson's theorem (queueing theory)) Jump to navigation Jump to search This article includes a list of general references, but it lacks sufficient corresponding inline citations. Please help to improve this article by introducing more precise citations. (June 2012) (Learn how and when to remove this template message) In queueing theory, a discipline within the mathematical theory of probability, a Jackson network (sometimes Jacksonian network[1]) is a class of queueing network where the equilibrium distribution is particularly simple to compute as the network has a product-form solution. It was the first significant development in the theory of networks of queues, and generalising and applying the ideas of the theorem to search for similar product-form solutions in other networks has been the subject of much research,[2] including ideas used in the development of the Internet.[3] The networks were first identified by James R. Jackson[4][5] and his paper was re-printed in the journal Management Science’s ‘Ten Most Influential Titles of Management Sciences First Fifty Years.’[6] Jackson was inspired by the work of Burke and Reich,[7] though Jean Walrand notes "product-form results … [are] a much less immediate result of the output theorem than Jackson himself appeared to believe in his fundamental paper".[8] An earlier product-form solution was found by R. R. P. Jackson for tandem queues (a finite chain of queues where each customer must visit each queue in order) and cyclic networks (a loop of queues where each customer must visit each queue in order).[9] A Jackson network consists of a number of nodes, where each node represents a queue in which the service rate can be both node-dependent (different nodes have different service rates) and state-dependent (service rates change depending on queue lengths). Jobs travel among the nodes following a fixed routing matrix. All jobs at each node belong to a single "class" and jobs follow the same service-time distribution and the same routing mechanism. Consequently, there is no notion of priority in serving the jobs: all jobs at each node are served on a first-come, first-served basis.

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] Contents 1 Necessary conditions for a Jackson network 2 Theorem 3 Definition 3.1 Theorem 3.2 Example 4 Generalized Jackson network 4.1 Brownian approximation 5 See also 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 {displaystyle P_{ij}} or leave the system with probability {displaystyle 1-sum _{j=1}^{m}P_{ij}} , which, 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 _{i}} 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 {displaystyle pi (k_{1},k_{2},ldots ,k_{m})=prod _{i=1}^{m}pi _{i}(k_{i})=prod _{i=1}^{m}[rho _{i}^{k_{i}}(1-rho _{i})].} The result {displaystyle pi (k_{1},k_{2},ldots ,k_{m})=prod _{i=1}^{m}pi _{i}(k_{i})} also holds for M/M/c model stations with ci servers at the {displaystyle i^{text{th}}} station, with utilization requirement {displaystyle rho _{i}0} . Each arrival is independently routed to node j with probability {displaystyle p_{0j}geq 0} and {displaystyle sum _{j=1}^{J}p_{0j}=1} . Upon service completion at node i, a job may go to another node j with probability {displaystyle p_{ij}} or leave the network with probability {displaystyle p_{i0}=1-sum _{j=1}^{J}p_{ij}} .

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

All jobs leave each node also following Poisson process, and define {displaystyle mu _{i}(x_{i})} as the service rate of node i when there are {displaystyle x_{i}} jobs at node i.

Let {displaystyle X_{i}(t)} denote the number of jobs at node i at time t, and {displaystyle mathbf {X} =(X_{i})_{i=1}^{J}} . Then the equilibrium distribution of {displaystyle mathbf {X} } , {displaystyle pi (mathbf {x} )=P(mathbf {X} =mathbf {x} )} is determined by the following system of balance equations: {displaystyle {begin{aligned}&pi (mathbf {x} )sum _{i=1}^{J}[alpha p_{0i}+mu _{i}(x_{i})(1-p_{ii})]\={}&sum _{i=1}^{J}[pi (mathbf {x} -mathbf {e} _{i})alpha p_{0i}+pi (mathbf {x} +mathbf {e} _{i})mu _{i}(x_{i}+1)p_{i0}]+sum _{i=1}^{J}sum _{jneq i}pi (mathbf {x} +mathbf {e} _{i}-mathbf {e} _{j})mu _{i}(x_{i}+1)p_{ij}.qquad (2)end{aligned}}} where {displaystyle mathbf {e} _{i}} denote the {displaystyle i^{text{th}}} unit vector.

Theorem Suppose a vector of independent random variables {displaystyle (Y_{1},ldots ,Y_{J})} with each {displaystyle Y_{i}} having a probability mass function as {displaystyle P(Y_{i}=n)=p(Y_{i}=0)cdot {frac {lambda _{i}^{n}}{M_{i}(n)}},quad (3)} where {displaystyle M_{i}(n)=prod _{j=1}^{n}mu _{i}(j)} . If {displaystyle sum _{n=1}^{infty }{frac {lambda _{i}^{n}}{M_{i}(n)}}0} Then by the theorem, we can calculate: {displaystyle lambda =(I-P^{T})^{-1}a={begin{bmatrix}1&0&0\-0.5&1&0\-0.5&0&1end{bmatrix}}^{-1}{begin{bmatrix}0.5times 5\0.5times 5\0end{bmatrix}}={begin{bmatrix}1&0&0\0.5&1&0\0.5&0&1end{bmatrix}}{begin{bmatrix}2.5\2.5\0end{bmatrix}}={begin{bmatrix}2.5\3.75\1.25end{bmatrix}}} According to the definition of {displaystyle mathbf {Y} } , we have: {displaystyle P(Y_{1}=0)=left(sum _{n=0}^{infty }left({frac {2.5}{15}}right)^{n}right)^{-1}={frac {5}{6}}} {displaystyle P(Y_{2}=0)=left(sum _{n=0}^{infty }left({frac {3.75}{12}}right)^{n}right)^{-1}={frac {11}{16}}} {displaystyle P(Y_{3}=0)=left(sum _{n=0}^{infty }left({frac {1.25}{10}}right)^{n}right)^{-1}={frac {7}{8}}} Hence the probability that there is one job at each node is: {displaystyle 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}}approx 0.00326} Since the service rate here does not depend on state, the {displaystyle Y_{i}} 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 general, 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 needed] {displaystyle Q(t)} of an open generalized Jackson network can be approximated by a reflected Brownian motion defined as {displaystyle operatorname {RBM} _{Q(0)}(theta ,Gamma ;R).} , where {displaystyle theta } is the drift of the process, {displaystyle Gamma } is the covariance matrix, and {displaystyle 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})mu } {displaystyle Gamma =(Gamma _{kell }){text{ with }}Gamma _{kell }=sum _{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^{T}} 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 =(mu )_{j=1}^{J}} a J-vector specifying the service rates of each node.

{displaystyle P} routing matrix.

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

{displaystyle c_{j}} variation of service time at {displaystyle j^{text{th}}} node.

{displaystyle c_{0,j}} variation of inter-arrival time at {displaystyle j^{text{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. (June 1976). "Networks of Queues". Advances in Applied Probability. 8 (2): 416–432. doi:10.2307/1425912. JSTOR 1425912. ^ Jackson, James R. (December 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. (December 2004). "Jobshop-Like Queueing Systems". Management Science. 50 (12): 1796–1802. doi:10.1287/mnsc.1040.0268. JSTOR 30046149. ^ Reich, Edgar (September 1957). "Waiting Times When Queues are in Tandem". Annals of Mathematical Statistics. 28 (3): 768. doi:10.1214/aoms/1177706889. JSTOR 2237237. ^ Walrand, Jean (November 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. (December 1984). "The Non-Ergodic Jackson Network". Journal of Applied Probability. 21 (4): 860–869. doi:10.2307/3213702. ^ Walrand, J.; Varaiya, P. (December 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 (unit)Erlang distributionFlow control (data)Message queueNetwork congestionNetwork schedulerPipeline (software)Quality of serviceScheduling (computing)Teletraffic engineering Category Categories: Queueing theory

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