Infinite-Server Queue Model $$MMAP_{k}(t)|G_{k}|\infty $$ with Time Varying Marked Map Arrivals of Customers and Occurrence of Catastrophes

2020 ◽  
pp. 171-184
Author(s):  
Ruben Kerobyan ◽  
Khanik Kerobyan ◽  
Carol Shubin ◽  
Phu Nguyen
Keyword(s):  
Time Varying ◽  
Infinite Server ◽  
Server Queue ◽  
Queue Model

Stationary Analysis of Infinite Server Queue with Batch Service

2021 ◽  
pp. 411-424
Author(s):  
Ayane Nakamura ◽  
Tuan Phung-Duc
Keyword(s):  
Batch Service ◽  
Infinite Server ◽  
Stationary Analysis ◽  
Server Queue

scholarly journals An infinite-server queue influenced by a semi-Markovian environment

2008 ◽  
Vol 61 (1) ◽  
pp. 65-84 ◽  
Author(s):  
Brian H. Fralix ◽  
Ivo J. B. F. Adan
Keyword(s):  
Markovian Environment ◽  
Infinite Server ◽  
Server Queue

Transient behavior of coverage processes by applications to the infinite-server queue

1993 ◽  
Vol 30 (3) ◽  
pp. 589-601 ◽  
Author(s):  
Sid Browne ◽  
J. Michael Steele
Keyword(s):  
Line Segment ◽  
Busy Period ◽  
Queueing System ◽  
Transient Behavior ◽  
Infinite Server ◽  
Coverage Process ◽  
Server Queue

We obtain the distribution of the length of a clump in a coverage process where the first line segment of a clump has a distribution that differs from the remaining segments of the clump. This result allows us to provide the distribution of the busy period in an M/G/∞ queueing system with exceptional first service, and applications are considered. The result also provides the tool necessary to analyze the transient behavior of an ordinary coverage process, namely the depletion time of the ordinary M/G/∞ system.

INFINITE-SERVER QUEUES WITH BATCH ARRIVALS AND DEPENDENT SERVICE TIMES

2012 ◽  
Vol 26 (2) ◽  
pp. 197-220 ◽  
Author(s):  
Guodong Pang ◽  
Ward Whitt
Keyword(s):  
Large Scale ◽  
Heavy Traffic ◽  
Arrival Rate ◽  
Arrival Process ◽  
Regularity Conditions ◽  
Time Varying ◽  
Performance Impact ◽  
Batch Arrivals ◽  
Infinite Server ◽  
Service Times

Motivated by large-scale service systems, we consider an infinite-server queue with batch arrivals, where the service times are dependent within each batch. We allow the arrival rate of batches to be time varying as well as constant. As regularity conditions, we require that the batch sizes be i.i.d. and independent of the arrival process of batches, and we require that the service times within different batches be independent. We exploit a recently established heavy-traffic limit for the number of busy servers to determine the performance impact of the dependence among the service times. The number of busy servers is approximately a Gaussian process. The dependence among the service times does not affect the mean number of busy servers, but it does affect the variance of the number of busy servers. Our approximations quantify the performance impact upon the variance. We conduct simulations to evaluate the heavy-traffic approximations for the stationary model and the model with a time-varying arrival rate. In the simulation experiments, we use the Marshall–Olkin multivariate exponential distribution to model dependent exponential service times within a batch. We also introduce a class of Marshall–Olkin multivariate hyperexponential distributions to model dependent hyper-exponential service times within a batch.

An infinite-server queue subject to an extraneous phase process and related models

1981 ◽  
Vol 18 (1) ◽  
pp. 236-244 ◽  
Author(s):  
P. Purdue ◽  
D. Linton
Keyword(s):  
Original System ◽  
Primary System ◽  
Secondary System ◽  
State Behavior ◽  
Infinite Server ◽  
Server Queue ◽  
Secondary Systems ◽  
Stage System ◽  
The Mean ◽  
Bulk Arrival

We consider an infinite-server queueing system in an extraneous environment. Initially it is shown that the systems of interest can be decomposed into a two-stage system. The primary system is an infinite-server queue with many customer types subject to a clearing mechanism. The secondary system is a special type of bulk-arrival, infinite-server queue. We derive results for the primary and secondary systems separately and combine the results to find the mean steady-state behavior of the original system.

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