The departure process of the M/G/1 queueing model with server vacation and exhaustive service discipline

1994 ◽  
Vol 31 (4) ◽  
pp. 1070-1082 ◽  
Author(s):  
Yinghui Tang
Keyword(s):  
Renewal Theory ◽  
Service Discipline ◽  
Time Interval ◽  
Single Server ◽  
Stieltjes Transform ◽  
Expected Number ◽  
Departure Process ◽  
Server Vacation ◽  
Server Queue ◽  
Interdeparture Time

In this paper we study the departure process of M/G/1 queueing models with a single server vacation and multiple server vacations. The arguments employed are direct probability decomposition, renewal theory and the Laplace–Stieltjes transform. We discuss the distribution of the interdeparture time and the expected number of departures occurring in the time interval (0, t] from the beginning of the state i (i = 0, 1, 2, ···), and provide a new method for analysis of the departure process of the single-server queue.

The departure process of the M/G/1 queueing model with server vacation and exhaustive service discipline

1994 ◽  
Vol 31 (04) ◽  
pp. 1070-1082 ◽  
Author(s):  
Yinghui Tang
Keyword(s):  
Renewal Theory ◽  
Service Discipline ◽  
Time Interval ◽  
Single Server ◽  
Stieltjes Transform ◽  
Expected Number ◽  
Departure Process ◽  
Server Vacation ◽  
Server Queue ◽  
Interdeparture Time

In this paper we study the departure process of M/G/1 queueing models with a single server vacation and multiple server vacations. The arguments employed are direct probability decomposition, renewal theory and the Laplace–Stieltjes transform. We discuss the distribution of the interdeparture time and the expected number of departures occurring in the time interval (0, t] from the beginning of the state i (i = 0, 1, 2, ···), and provide a new method for analysis of the departure process of the single-server queue.

scholarly journals The BMAP/G/1 vacation queue with queue-length dependent vacation schedule

1998 ◽  
Vol 40 (2) ◽  
pp. 207-221 ◽  
Author(s):  
Yang Woo Shin ◽  
Chareles E. M. Pearce
Keyword(s):  
Queue Length ◽  
Length Distribution ◽  
Arrival Process ◽  
Service Discipline ◽  
Single Server ◽  
Server Vacation ◽  
Batch Markovian Arrival Process ◽  
Queue Length Distribution ◽  
Special Cases ◽  
Number Of Customers

AbstractWe treat a single-server vacation queue with queue-length dependent vacation schedules. This subsumes the single-server vacation queue with exhaustive service discipline and the vacation queue with Bernoulli schedule as special cases. The lengths of vacation times depend on the number of customers in the system at the beginning of a vacation. The arrival process is a batch-Markovian arrival process (BMAP). We derive the queue-length distribution at departure epochs. By using a semi-Markov process technique, we obtain the Laplace-Stieltjes transform of the transient queue-length distribution at an arbitrary time point and its limiting distribution

Logarithmic asymptotics for steady-state tail probabilities in a single-server queue

1994 ◽  
Vol 31 (A) ◽  
pp. 131-156 ◽  
Author(s):  
Peter W. Glynn ◽  
Ward Whitt
Keyword(s):  
Steady State ◽  
Decay Rate ◽  
Partial Sums ◽  
Asymptotic Result ◽  
Service Discipline ◽  
Single Server ◽  
Single Server Queue ◽  
Asymptotic Decay ◽  
Server Queue ◽  
Service Times

We consider the standard single-server queue with unlimited waiting space and the first-in first-out service discipline, but without any explicit independence conditions on the interarrival and service times. We find conditions for the steady-state waiting-time distribution to have asymptotics of the form x–1 log P(W> x) → –θ ∗as x → ∞for θ ∗ > 0. We require only stationarity of the basic sequence of service times minus interarrival times and a Gärtner–Ellis condition for the cumulant generating function of the associated partial sums, i.e. n–1 log E exp (θSn) → ψ (θ) as n → ∞, plus regularity conditions on the decay rate function ψ. The asymptotic decay rate θ is the root of the equation ψ (θ) = 0. This result in turn implies a corresponding asymptotic result for the steady-state workload in a queue with general non-decreasing input. This asymptotic result covers the case of multiple independent sources, so that it provides additional theoretical support for a concept of effective bandwidths for admission control in multiclass queues based on asymptotic decay rates.

On the integral of the workload process of the single server queue

2003 ◽  
Vol 40 (01) ◽  
pp. 200-225 ◽  
Author(s):  
A. A. Borovkov ◽  
O. J. Boxma ◽  
Z. Palmowski
Keyword(s):  
Service Time ◽  
Time Distribution ◽  
Busy Period ◽  
Single Server ◽  
Service Time Distribution ◽  
Whittaker Functions ◽  
Single Server Queue ◽  
Stieltjes Transform ◽  
Server Queue ◽  
Sequential Approximation

This paper is devoted to a study of the integral of the workload process of the single server queue, in particular during one busy period. Firstly, we find asymptotics of the area 𝒜 swept under the workload process W(t) during the busy period when the service time distribution has a regularly varying tail. We also investigate the case of a light-tailed service time distribution. Secondly, we consider the problem of obtaining an explicit expression for the distribution of 𝒜. In the general GI/G/1 case, we use a sequential approximation to find the Laplace—Stieltjes transform of 𝒜. In the M/M/1 case, this transform is obtained explicitly in terms of Whittaker functions. Thirdly, we consider moments of 𝒜 in the GI/G/1 queue. Finally, we show asymptotic normality of .

Losses per cycle in a single-server queue

2002 ◽  
Vol 39 (04) ◽  
pp. 905-909 ◽  
Author(s):  
Ronald W. Wolff
Keyword(s):  
Elementary Proof ◽  
Arbitrary Order ◽  
Batch Size ◽  
Single Server ◽  
Expected Number ◽  
Batch Arrivals ◽  
Service Interruption ◽  
Server Queue ◽  
Service Rates ◽  
Busy Cycle

Several recent papers have shown that for the M/G/1/n queue with equal arrival and service rates, the expected number of lost customers per busy cycle is equal to 1 for every n ≥ 0. We present an elementary proof based on Wald's equation and, for GI/G/1/n, obtain conditions for this quantity to be either less than or greater than 1 for every n ≥ 0. In addition, we extend this result to batch arrivals, where, for average batch size β, the same quantity is either less than or greater than β. We then extend these results to general ways that customers may be lost, to an arbitrary order of service that allows service interruption, and finally to reneging.

scholarly journals Queues, stores, and tableaux

2005 ◽  
Vol 42 (04) ◽  
pp. 1145-1167 ◽  
Author(s):  
Moez Draief ◽  
Jean Mairesse ◽  
Neil O'Connell
Keyword(s):  
Stability Condition ◽  
Young Tableau ◽  
Arrival Process ◽  
Single Server ◽  
Single Server Queue ◽  
Departure Process ◽  
Transient Behaviour ◽  
Server Queue ◽  
The Stability ◽  
First In First Out

Consider the single-server queue with an infinite buffer and a first-in–first-out discipline, either of type M/M/1 or Geom/Geom/1. Denote by 𝒜 the arrival process and by s the services. Assume the stability condition to be satisfied. Denote by 𝒟 the departure process in equilibrium and by r the time spent by the customers at the very back of the queue. We prove that (𝒟, r) has the same law as (𝒜, s), which is an extension of the classical Burke theorem. In fact, r can be viewed as the sequence of departures from a dual storage model. This duality between the two models also appears when studying the transient behaviour of a tandem by means of the Robinson–Schensted–Knuth algorithm: the first and last rows of the resulting semistandard Young tableau are respectively the last instant of departure from the queue and the total number of departures from the store.

On the integral of the workload process of the single server queue

2003 ◽  
Vol 40 (1) ◽  
pp. 200-225 ◽  
Author(s):  
A. A. Borovkov ◽  
O. J. Boxma ◽  
Z. Palmowski
Keyword(s):  
Service Time ◽  
Time Distribution ◽  
Busy Period ◽  
Single Server ◽  
Service Time Distribution ◽  
Whittaker Functions ◽  
Single Server Queue ◽  
Stieltjes Transform ◽  
Server Queue ◽  
Sequential Approximation

This paper is devoted to a study of the integral of the workload process of the single server queue, in particular during one busy period. Firstly, we find asymptotics of the area 𝒜 swept under the workload process W(t) during the busy period when the service time distribution has a regularly varying tail. We also investigate the case of a light-tailed service time distribution. Secondly, we consider the problem of obtaining an explicit expression for the distribution of 𝒜. In the general GI/G/1 case, we use a sequential approximation to find the Laplace—Stieltjes transform of 𝒜. In the M/M/1 case, this transform is obtained explicitly in terms of Whittaker functions. Thirdly, we consider moments of 𝒜 in the GI/G/1 queue. Finally, we show asymptotic normality of .

Losses per cycle in a single-server queue

2002 ◽  
Vol 39 (4) ◽  
pp. 905-909 ◽  
Author(s):  
Ronald W. Wolff
Keyword(s):  
Elementary Proof ◽  
Arbitrary Order ◽  
Batch Size ◽  
Single Server ◽  
Expected Number ◽  
Batch Arrivals ◽  
Service Interruption ◽  
Server Queue ◽  
Service Rates ◽  
Busy Cycle

Several recent papers have shown that for the M/G/1/nqueue with equal arrival and service rates, the expected number of lost customers per busy cycle is equal to 1 for everyn≥ 0. We present an elementary proof based on Wald's equation and, for GI/G/1/n, obtain conditions for this quantity to be either less than or greater than 1 for everyn≥ 0. In addition, we extend this result to batch arrivals, where, for average batch size β, the same quantity is either less than or greater than β. We then extend these results to general ways that customers may be lost, to an arbitrary order of service that allows service interruption, and finally to reneging.

Logarithmic asymptotics for steady-state tail probabilities in a single-server queue

1994 ◽  
Vol 31 (A) ◽  
pp. 131-156 ◽  
Author(s):  
Peter W. Glynn ◽  
Ward Whitt
Keyword(s):  
Steady State ◽  
Decay Rate ◽  
Partial Sums ◽  
Asymptotic Result ◽  
Service Discipline ◽  
Single Server ◽  
Single Server Queue ◽  
Asymptotic Decay ◽  
Server Queue ◽  
Service Times

We consider the standard single-server queue with unlimited waiting space and the first-in first-out service discipline, but without any explicit independence conditions on the interarrival and service times. We find conditions for the steady-state waiting-time distribution to have asymptotics of the form x –1 log P(W > x) → –θ ∗as x → ∞for θ ∗ > 0. We require only stationarity of the basic sequence of service times minus interarrival times and a Gärtner–Ellis condition for the cumulant generating function of the associated partial sums, i.e. n –1 log E exp (θSn ) → ψ (θ) as n → ∞, plus regularity conditions on the decay rate function ψ. The asymptotic decay rate θ is the root of the equation ψ (θ) = 0. This result in turn implies a corresponding asymptotic result for the steady-state workload in a queue with general non-decreasing input. This asymptotic result covers the case of multiple independent sources, so that it provides additional theoretical support for a concept of effective bandwidths for admission control in multiclass queues based on asymptotic decay rates.

Constrained Load-Balancing Policies for Parallel Single-Server Queue Systems

2020 ◽  
Vol 66 (8) ◽  
pp. 3501-3527 ◽  
Author(s):  
Hung T. Do ◽  
Masha Shunko
Keyword(s):  
Load Balancing ◽  
Performance Improvement ◽  
Queueing Systems ◽  
Arrival Rate ◽  
Service Level ◽  
Single Server ◽  
Objective Functions ◽  
Expected Number ◽  
Server Queue ◽  
Number Of Customers

Flow-control policies that balance server loads are well known for improving performance of queueing systems with multiple nodes. However, although load balancing benefits the system overall, it may negatively impact some of the queueing nodes. For example, it may reduce throughput rates or engender unfairness with respect to some performance measures. For queueing systems with multiple single-server nodes, we propose a set of constrained load-balancing policies that ensures the expected arrival rate to each queueing node is not reduced, and we show that such policies provide multiple benefits for each queueing node: stochastically fewer customers and lower variance of the number of customers at each queueing node. These results imply performance improvement as measured by multiple general objective functions, including but not limited to the expected number of customers at a queueing node, probability of having a high number of customers, variance of the number of customers, and expected number of customers conditional on exceeding a threshold defined by a fixed service level. We demonstrate numerically that our proposed policies capture a large portion of the potential maximal improvement. This paper was accepted by Noah Gans, stochastic models and simulation.

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