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.
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.
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.
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.
<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>
Unified and refined analysis of the response time and waiting time in the M/M/m FCFS preemptive-resume priority queue