scholarly journals Delay Analysis of anM/G/1/KPriority Queueing System with Push-out Scheme

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Yutae Lee ◽  
Bong Dae Choi ◽  
Bara Kim ◽  
Dan Keun Sung
Keyword(s):  
Communication Networks ◽  
Waiting Time ◽  
Queueing System ◽  
Waiting Times ◽  
Loss Probability ◽  
Mean Waiting Time ◽  
The Mean ◽  
Recursive Equations ◽  
Push Out ◽  
Stieltjes Transforms

This paper considers anM/G/1/Kqueueing system with push-out scheme which is one of the loss priority controls at a multiplexer in communication networks. The loss probability for the model with push-out scheme has been analyzed, but the waiting times are not available for the model. Using a set of recursive equations, this paper derives the Laplace-Stieltjes transforms (LSTs) of the waiting time and the push-out time of low-priority messages. These results are then utilized to derive the loss probability of each traffic type and the mean waiting time of high-priority messages. Finally, some numerical examples are provided.

scholarly journals Some reflections on the Renewal-theory paradox in queueing theory

1998 ◽  
Vol 11 (3) ◽  
pp. 355-368 ◽  
Author(s):  
Robert B. Cooper ◽  
Shun-Chen Niu ◽  
Mandyam M. Srinivasan
Keyword(s):  
Waiting Time ◽  
Queueing Theory ◽  
Waiting Times ◽  
Renewal Theory ◽  
Vacation Models ◽  
Polling Models ◽  
Mean Waiting Time ◽  
The Mean ◽  
Expository Paper ◽  
Set Up

The classical renewal-theory (waiting time, or inspection) paradox states that the length of the renewal interval that covers a randomly-selected time epoch tends to be longer than an ordinary renewal interval. This paradox manifests itself in numerous interesting ways in queueing theory, a prime example being the celebrated Pollaczek-Khintchine formula for the mean waiting time in the M/G/1 queue. In this expository paper, we give intuitive arguments that “explain” why the renewal-theory paradox is ubiquitous in queueing theory, and why it sometimes produces anomalous results. In particular, we use these intuitive arguments to explain decomposition in vacation models, and to derive formulas that describe some recently-discovered counterintuitive results for polling models, such as the reduction of waiting times as a consequence of forcing the server to set up even when no work is waiting.

On the stochastic ordering of waiting times for patterns in sequences of random digits

1984 ◽  
Vol 21 (4) ◽  
pp. 730-737 ◽  
Author(s):  
Gunnar Blom
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Stochastic Ordering ◽  
Mean Waiting Time ◽  
Coin Tossing ◽  
The Mean

Random digits are collected one at a time until a pattern with given digits is obtained. Blom (1982) and others have determined the mean waiting time for such a pattern. It is proved that when a given pattern has larger mean waiting time than another pattern, then the waiting time for the former is stochastically larger than that for the latter. An application is given to a coin-tossing game.

The general N server finite queue

1971 ◽  
Vol 8 (04) ◽  
pp. 828-834 ◽  
Author(s):  
Asha Seth Kapadia
Keyword(s):  
Waiting Time ◽  
Busy Period ◽  
Queueing System ◽  
List Type ◽  
Type Number ◽  
Stochastic Nature ◽  
Mean Waiting Time ◽  
Queue Discipline ◽  
Mean And Variance ◽  
The Mean

Kingman (1962) studied the effect of queue discipline on the mean and variance of the waiting time. He made no assumptions regarding the stochastic nature of the input and the service distributions, except that the input and service processes are independent of each other. When the following two conditions hold: (a) no server sits idle while there are customers waiting to be served; (b) the busy period is finite with probability one (i.e., the queue empties infinitely often with probability one); he has shown that the mean waiting time is independent of the queue discipline and the variance of the waiting time is a minimum when the customers are served in order of their arrival. Conditions (a) and (b) will henceforward be called Kingman conditions and a queueing system satisfying Kingman conditions will be referred to in the text as a Kingman queue.

On a property of the variance of the waiting time of a queue

1968 ◽  
Vol 5 (3) ◽  
pp. 702-703 ◽  
Author(s):  
D. G. Tambouratzis
Keyword(s):  
Waiting Time ◽  
Queueing System ◽  
Mean Waiting Time ◽  
Queue Discipline ◽  
The Mean ◽  
Number Of Customers

In this note, we consider a queueing system under any discipline which does not affect the distribution of the number of customers in the queue at any time. We shall show that the variance of the waiting time is a maximum when the queue discipline is “last come, first served”. This result complements that of Kingman [1] who showed that, under the same assumptions, the mean waiting time is independent of the queue discipline and the variance of the waiting time is a minimum when the customers are served in the order of their arrival.

THEORETICAL ANALYSIS OF MEAN WAITING TIME FOR MESSAGE DELIVERY IN LATTICE AD HOC NETWORKS

2010 ◽  
Vol 19 (08) ◽  
pp. 1711-1741
Author(s):  
AKIRA OTSUKA ◽  
KEISUKE NAKANO ◽  
KAZUYUKI MIYAKITA
Keyword(s):  
Ad Hoc Networks ◽  
Waiting Time ◽  
Ad Hoc ◽  
Waiting Times ◽  
Traffic Patterns ◽  
Mobile Nodes ◽  
Approximate Methods ◽  
Mean Waiting Time ◽  
The Mean ◽  
Hoc Networks

In ad hoc networks, the analysis of connectivity performance is crucial. The waiting time to deliver message M from source S to destination D is a measure of connectivity that reflects the effects of mobility, and some approximate methods have been proposed to theoretically analyze the mean waiting time in one-dimensional ad hoc networks that consist of mobile nodes moving along a street. In this paper, we extend these approximate methods to analyze the mean waiting time in two-dimensional networks with a lattice structure with various flows of mobile nodes. We discuss how the mean waiting times behave in such complicated street networks and how to approximate two kinds of mean waiting times. We show that our approximate methods can successfully compute the mean waiting times for even traffic patterns and roughly estimate them for uneven traffic patterns in two-dimensional lattice networks. In these analyses, we consider two shadowing models to investigate how shadowing affects the waiting time. We also discuss the effect of different positions of S on the mean waiting time.

scholarly journals Managing Queues with Heterogeneous Servers

2011 ◽  
Vol 48 (2) ◽  
pp. 435-452 ◽  
Author(s):  
Jung Hyun Kim ◽  
Hyun-Soo Ahn ◽  
Rhonda Righter
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Structural Result ◽  
Batch Arrivals ◽  
Optimal Thresholds ◽  
Mean Waiting Time ◽  
Server Problem ◽  
The Mean ◽  
The Impact ◽  
Intuitive Proof

We consider several versions of the job assignment problem for an M/M/m queue with servers of different speeds. When there are two classes of customers, primary and secondary, the number of secondary customers is infinite, and idling is not permitted, we develop an intuitive proof that the optimal policy that minimizes the mean waiting time has a threshold structure. That is, for each server, there is a server-dependent threshold such that a primary customer will be assigned to that server if and only if the queue length of primary customers meets or exceeds the threshold. Our key argument can be generalized to extend the structural result to models with impatient customers, discounted waiting time, batch arrivals and services, geometrically distributed service times, and a random environment. We show how to compute the optimal thresholds, and study the impact of heterogeneity in server speeds on mean waiting times. We also apply the same machinery to the classical slow-server problem without secondary customers, and obtain more general results for the two-server case and strengthen existing results for more than two servers.

The general N server finite queue

1971 ◽  
Vol 8 (4) ◽  
pp. 828-834 ◽  
Author(s):  
Asha Seth Kapadia
Keyword(s):  
Waiting Time ◽  
Busy Period ◽  
Queueing System ◽  
List Type ◽  
Type Number ◽  
Stochastic Nature ◽  
Mean Waiting Time ◽  
Queue Discipline ◽  
Mean And Variance ◽  
The Mean

Kingman (1962) studied the effect of queue discipline on the mean and variance of the waiting time. He made no assumptions regarding the stochastic nature of the input and the service distributions, except that the input and service processes are independent of each other. When the following two conditions hold: (a)no server sits idle while there are customers waiting to be served;(b)the busy period is finite with probability one (i.e., the queue empties infinitely often with probability one); he has shown that the mean waiting time is independent of the queue discipline and the variance of the waiting time is a minimum when the customers are served in order of their arrival. Conditions (a) and (b) will henceforward be called Kingman conditions and a queueing system satisfying Kingman conditions will be referred to in the text as a Kingman queue.

On the stochastic ordering of waiting times for patterns in sequences of random digits

1984 ◽  
Vol 21 (04) ◽  
pp. 730-737 ◽  
Author(s):  
Gunnar Blom
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Stochastic Ordering ◽  
Mean Waiting Time ◽  
Coin Tossing ◽  
The Mean

Random digits are collected one at a time until a pattern with given digits is obtained. Blom (1982) and others have determined the mean waiting time for such a pattern. It is proved that when a given pattern has larger mean waiting time than another pattern, then the waiting time for the former is stochastically larger than that for the latter. An application is given to a coin-tossing game.

On a property of the variance of the waiting time of a queue

1968 ◽  
Vol 5 (03) ◽  
pp. 702-703 ◽  
Author(s):  
D. G. Tambouratzis
Keyword(s):  
Waiting Time ◽  
Queueing System ◽  
Mean Waiting Time ◽  
Queue Discipline ◽  
The Mean ◽  
Number Of Customers

In this note, we consider a queueing system under any discipline which does not affect the distribution of the number of customers in the queue at any time. We shall show that the variance of the waiting time is a maximum when the queue discipline is “last come, first served”. This result complements that of Kingman [1] who showed that, under the same assumptions, the mean waiting time is independent of the queue discipline and the variance of the waiting time is a minimum when the customers are served in the order of their arrival.

scholarly journals An audit of waiting times at a specialist psychotherapy service

2006 ◽  
Vol 30 (5) ◽  
pp. 182-184 ◽  
Author(s):  
Ged Garry ◽  
Graham Paley
Keyword(s):  
Service Delivery ◽  
Waiting Time ◽  
Action Plan ◽  
Waiting Times ◽  
Clinical Implications ◽  
Audit Process ◽  
Average Waiting Time ◽  
Mean Waiting Time ◽  
The Mean ◽  
Using Data

Aims and MethodReferrals to a specialist psychotherapy service were audited to measure the average waiting time for a first appointment and the proportion of patients waiting longer than 13 weeks. Recommendations for improving service delivery were made, an action plan implemented and the audit repeated.ResultsIn 2003, an initial audit of 355 referrals was completed using data from 2002. This found a mean waiting time to first appointment of 11.5 weeks with 30% of patients waiting longer than 13 weeks. In 2004, following implementation of the action plan, a re-audit of 200 patients found that the mean waiting time from receipt of referral to first appointment had reduced to 6.7 weeks with only 2.3% waiting more than 13 weeks.Clinical ImplicationsAudit can improve the efficiency of service delivery in a specialist psychotherapy service. However, this may require that psychotherapists review traditional ways of working. Also, it is important that they feel personally involved in the audit process.

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