skip to main content
short-paper

On estimation problems for the G/G/∞ Queue

Authors Info & Claims
Published:21 December 2011Publication History
Skip Abstract Section

Abstract

We consider estimation problems in G/G/∞ queue under incomplete information. Specifically, we are interested in scenarios where it is infeasible to track each individual job in the system and only aggregate statistics are known or observable. We first show that the minimum expected square estimator for the queue length process can be written as the sum of the minimum square estimators for an indicator function associated with each job, which is simply the survival function of the service time variable for each job. We also obtain tight lower and upper bounds on the time average of the square estimation error. Next we look at the inverse problem of estimating the service time distribution when the observed process is only the queue length process. We develop an on-line stochastic-optimization based estimation algorithm for the service time distribution and study its convergence under different parameter settings.

References

  1. N. H. Bingham and Susan M. Pitts. Non-Parametric Estimation for the M/G/∞ Queue. Ann. Inst. Statis. Math., 51(1):71--97, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  2. M. Brown. An M/G/∞ Estimation Problem. Ann. Math. Statist, 41:651--654, 1970.Google ScholarGoogle ScholarCross RefCross Ref
  3. Hanhua Feng and Vishal Misra. On the Relationship Between Coefficient of Variation and Performance of M/G/1 -- FB Queues. SIGMETRICS Performance Evaluation Review, 32(2):17--19, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Rudolf Grubel and Susan M. Pitts. Non-Parameteric Estimation in Renewal Theory, I: the empirical renewal function. The Annals of Statistics, 21:1431--1451, 1993.Google ScholarGoogle ScholarCross RefCross Ref
  5. Richard C. Larson. The queue inference engine: Deducing queue statistics from transactional data. Management Science, 36(5):586--601, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. A. Nozari and Whitt W. Estimating Average Production Intervals Using Inventory Measurements: Little's Law for Partially Observable Processes. Operations Research, 36:308--323, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. Pickands and R. A. Stine. Estimation for an M/G/∞ Queue with Incomplete Information. Biometrika, 84:295--308, 1997.Google ScholarGoogle ScholarCross RefCross Ref

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

  • Article Metrics

    • Downloads (Last 12 months)1
    • Downloads (Last 6 weeks)0

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader