G/G/1 queue

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In queueing theory, a discipline within the mathematical theory of probability, the G/G/1 queue represents the queue length in a system with a single server where interarrival times have a general (meaning arbitrary) distribution and service times have a (different) general distribution.[1] The evolution of the queue can be described by the Lindley equation.[2]

The system is described in Kendall's notation where the G denotes a general distribution for both interarrival times and service times and the 1 that the model has a single server.[3][4] Different interarrival and service times are considered to be independent, and sometimes the model is denoted GI/GI/1 to emphasise this.

Waiting time[edit]

Kingman's formula gives an approximation for the mean waiting time in a G/G/1 queue.[5] Lindley's integral equation is a relationship satisfied by the stationary waiting time distribution which can be solved using the Wiener–Hopf method.[6]

Multiple servers[edit]

Few results are known for the general G/G/k model as it generalises the M/G/k queue for which few metrics are known. Bounds can be computed using mean value analysis techniques, adapting results from the M/M/c queue model, using heavy traffic approximations, empirical results[7]:189[8] or approximating distributions by phase type distributions and then using matrix analytic methods to solve the approximate systems.[7]:201

In a G/G/2 queue with heavy-tailed job sizes, the tail of the delay time distribution is known to behave like the tail of an exponential distribution squared under low loads and like the tail of an exponential distribution for high loads.[9][10][11]

References[edit]

  1. ^ Bhat, U. N. (2008). "The General Queue G/G/1 and Approximations". An Introduction to Queueing Theory. pp. 169–183. doi:10.1007/978-0-8176-4725-4_9. ISBN 978-0-8176-4724-7. 
  2. ^ Foss, S. (2011). "The G/G/1 Queue". Wiley Encyclopedia of Operations Research and Management Science. doi:10.1002/9780470400531.eorms0878. ISBN 9780470400531. 
  3. ^ Kendall, D. G. (1953). "Stochastic Processes Occurring in the Theory of Queues and their Analysis by the Method of the Imbedded Markov Chain". The Annals of Mathematical Statistics. 24 (3): 338. doi:10.1214/aoms/1177728975. JSTOR 2236285. 
  4. ^ Smith, W. L. (1953). "On the distribution of queueing times". Mathematical Proceedings of the Cambridge Philosophical Society. 49 (3): 449. doi:10.1017/S0305004100028620. 
  5. ^ Kingman, J. F. C.; Atiyah (October 1961). "The single server queue in heavy traffic". Mathematical Proceedings of the Cambridge Philosophical Society. 57 (4): 902. doi:10.1017/S0305004100036094. JSTOR 2984229. 
  6. ^ Prabhu, N. U. (1974). "Wiener-Hopf Techniques in Queueing Theory". Mathematical Methods in Queueing Theory. Lecture Notes in Economics and Mathematical Systems. 98. pp. 81–90. doi:10.1007/978-3-642-80838-8_5. ISBN 978-3-540-06763-4. 
  7. ^ a b Gautam, Natarajan (2012). Analysis of Queues: Methods and Applications. CRC Press. ISBN 9781439806586. 
  8. ^ Whitt, W. (2009). "Approximations for the GI/G/m Queue" (PDF). Production and Operations Management. 2 (2): 114–161. doi:10.1111/j.1937-5956.1993.tb00094.x. 
  9. ^ Harchol-Balter, M. (2012). "Task Assignment Policies for Server Farms". Performance Modeling and Design of Computer Systems. p. 408. doi:10.1017/CBO9781139226424.031. ISBN 9781139226424. 
  10. ^ Whitt, W. (2000). "The impact of a heavy-tailed service-time distribution upon the M/GI/s waiting-time distribution" (PDF). Queueing Systems. 36: 71–87. doi:10.1023/A:1019143505968. 
  11. ^ Foss, S.; Korshunov, D. (2006). "Heavy Tails in Multi-Server Queue". Queueing Systems. 52: 31. arXiv:1303.4705free to read. doi:10.1007/s11134-006-3613-z.