scholarly journals Unified and refined analysis of the response time and waiting time in the M/M/m FCFS preemptive-resume priority queue

2017 ◽  
Vol 13 (4) ◽  
pp. 1945-1973
Author(s):  
Hideaki Takagi ◽  
Keyword(s):  
Response Time ◽  
Waiting Time ◽  
Priority Queue ◽  
Preemptive Resume ◽  
Preemptive Resume Priority

On the work load process in a general preemptive resume priority queue

1972 ◽  
Vol 9 (03) ◽  
pp. 588-603 ◽  
Author(s):  
R. Schassberger
Keyword(s):  
Distribution Function ◽  
Work Load ◽  
Priority Queue ◽  
General Distribution ◽  
Time Service ◽  
Service Unit ◽  
System A ◽  
Preemptive Resume ◽  
Preemptive Resume Priority ◽  
Method Of Phases

Consider the following queuing system: A sequence of customers arrive at a service unit in a recurrent stream. A customer is of priority k with probability πk , k = 1, …, n. A class i customer preempts service of class k, k > i. Interrupted service is resumed without loss or gain in service time. Service is FIFO within classes. Service times for class k are drawn from a general distribution function Bk (t). Using the method of phases and a resolution technique from the theory of Markov processes we obtain Laplace transforms of various distributions.

Preemptive Resume Priority Queue

1961 ◽  
Vol 9 (5) ◽  
pp. 732-742 ◽  
Author(s):  
N. K. Jaiswal
Keyword(s):  
Priority Queue ◽  
Preemptive Resume ◽  
Preemptive Resume Priority

On the work load process in a general preemptive resume priority queue

1972 ◽  
Vol 9 (3) ◽  
pp. 588-603 ◽  
Author(s):  
R. Schassberger
Keyword(s):  
Distribution Function ◽  
Work Load ◽  
Priority Queue ◽  
General Distribution ◽  
Time Service ◽  
Service Unit ◽  
System A ◽  
Preemptive Resume ◽  
Preemptive Resume Priority ◽  
Method Of Phases

Consider the following queuing system: A sequence of customers arrive at a service unit in a recurrent stream. A customer is of priority k with probability πk, k = 1, …, n. A class i customer preempts service of class k, k > i. Interrupted service is resumed without loss or gain in service time. Service is FIFO within classes. Service times for class k are drawn from a general distribution function Bk(t).Using the method of phases and a resolution technique from the theory of Markov processes we obtain Laplace transforms of various distributions.

scholarly journals The MAP, M/G1,G2/1 queue with preemptive priority

1997 ◽  
Vol 10 (4) ◽  
pp. 407-421 ◽  
Author(s):  
Bong Dae Choi ◽  
Gang Uk Hwang
Keyword(s):  
Service Time ◽  
Probability Generating Function ◽  
Priority Queue ◽  
Arrival Process ◽  
Supplementary Variable ◽  
Preemptive Priority ◽  
Preemptive Resume ◽  
Preemptive Resume Priority ◽  
Number Of Customers ◽  
Time Density

We consider the MAP, M/G1,G2/1 queue with preemptive resume priority, where low priority customers arrive to the system according to a Markovian arrival process (MAP) and high priority customers according to a Poisson process. The service time density function of low (respectively: high) priority customers is g1(x) (respectively: g2(x)). We use the supplementary variable method with Extended Laplace Transforms to obtain the joint transform of the number of customers in each priority queue, as well as the remaining service time for the customer in service in the steady state. We also derive the probability generating function for the number of customers of low (respectively, high) priority in the system just after the service completion epochs for customers of low (respectively, high) priority.

scholarly journals Diffusion Model of Preemptive-Resume Priority Systems and Its Application to Performance Evaluation of SDN Switches

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5042
Author(s):  
Tomasz Nycz ◽  
Tadeusz Czachórski ◽  
Monika Nycz
Keyword(s):  
Diffusion Approximation ◽  
Numerical Models ◽  
Software Defined Networks ◽  
Detailed Model ◽  
Preemptive Resume ◽  
Central Controller ◽  
Priority Systems ◽  
Primary Element ◽  
Preemptive Resume Priority ◽  
General Distributions

The increasing use of Software-Defined Networks brings the need for their performance analysis and detailed analytical and numerical models of them. The primary element of such research is a model of a SDN switch. This model should take into account non-Poisson traffic and general distributions of service times. Because of frequent changes in SDN flows, it should also analyze transient states of the queues. The method of diffusion approximation can meet these requirements. We present here a diffusion approximation of priority queues and apply it to build a more detailed model of SDN switch where packets returned by the central controller have higher priority than other packets.

scholarly journals SIMULASI WAKTU TUNGGU MAHASISWA DAN DOSEN DI LAYANAN AKADEMIK UNIVERSITAS BUNDA MULIA

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Rudy Santosa Sudirga
Keyword(s):  
Response Time ◽  
Waiting Time ◽  
Operations Management ◽  
Heavy Traffic ◽  
Simulation Models ◽  
Operation Performance ◽  
Service Operation ◽  
Mean And Variance ◽  
The Mean ◽  
Time In Service

<p>The Management of Academic Service continues to be a major challenge for many college, high school and college organizations in providing better services with fewer resources. The allocation of service staffs and response-time in service involve many challenging issues, because the mean and variance of the response-time in service can be increased dramatically with the intensity of heavy traffic. This study discusses how to use simulation models to improve response time in service operation. Performance at the Academic Service as a whole can be considered very good and is still idle due to utilization of Academic Service, which is still equal to an average of 17%, or it can be said that the workload is not too excessive and deemed to be able to serve the students and lecturers. The performance of Academic Sevice University Bunda Mulia can be considered excellent in terms of operations management, as indicated by the average waiting time, which is very short at only 9.10 seconds.<br />Keywords: Queueing System, Waiting Time, and Simulation</p>

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