Different Classes Of Customers Research Articles

Process Scheduling in a Computer System

A computer system is modeled by an exponential queueing network with different classes of customers and a general class of service policies. The mean cost per unit time is taken as the loss function. Three lower bounds of the loss function under the whole class of service policies, as well as an upper bound of the minimal loss, are derived. The loss function is expressed in a convenient form from which we derive a simple heuristic service so called Klimov policy. This is applied to several examples of computer systems and is evaluated by the bounds. The examples recommend using Klimov policy in any case; so we have a first rule for deciding how to provide service capacity to different customers in a network of queues.

A Numerical Method to Obtain the Equilibrium Results for the Multiple Finite Source Priority Queueing Model

In this paper a numerical method is presented to obtain the equilibrium results of the multiple finite source queueing model having a single server using a nonpreemptive fixed priority service discipline and where each class of customers has a different exponential interarrival density function and arbitrary service time distribution function. This solution method uses the method of imbedded Markov Chain and the renewal reward theorem to find the proportion of time the server is busy with the customers of different classes. The equilibrium results are then related to these proportions using an extension of Little's formula.

Open, Closed, and Mixed Networks of Queues with Different Classes of Customers

article Free Access Share on Open, Closed, and Mixed Networks of Queues with Different Classes of Customers Authors: Forest Baskett Departments of Computer Science and Electrical Engineering, Stanford University, Stanford, CA Departments of Computer Science and Electrical Engineering, Stanford University, Stanford, CAView Profile , K. Mani Chandy Department of Computer Sciences, University of Texas at Austin, Austin, TX Department of Computer Sciences, University of Texas at Austin, Austin, TXView Profile , Richard R. Muntz Department of Computer Science, University of California, Los Angeles, CA Department of Computer Science, University of California, Los Angeles, CAView Profile , Fernando G. Palacios Department of Computer Sciences, University of Texas at Austin, Austin, TX Department of Computer Sciences, University of Texas at Austin, Austin, TXView Profile Authors Info & Claims Journal of the ACMVolume 22Issue 2April 1975 pp 248–260https://doi.org/10.1145/321879.321887Published:01 April 1975Publication History 1,966citation5,251DownloadsMetricsTotal Citations1,966Total Downloads5,251Last 12 Months335Last 6 weeks50 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Queuing Models with Lane Selection: A New Class of Problems

This paper presents a new class of queuing models. There are n distinct types of customers and n distinct types of service facilities. Some customers can be processed at any service facility, but other customer types can only use selected ones among the service-facility types. This class of model is named “lane selection,” abbreviated LS, since the arriving customers with some freedom of choice must select the queue, or lane, in which they are to be processed through the system. It is shown by example that many physical situations are represented more accurately by this model than by more conventional ones. However, in analysis, the LS concept raises many theoretical difficulties. For a number of special cases, this paper obtains in explicit form as functions of the model parameters the mean queue lengths and waiting times for the different classes of customers.

Distribution of the Proceeds of Securities of Different Classes of Customers Wrongfully Pledged by a Broker

Distribution of the Proceeds of Securities of Different Classes of Customers Wrongfully Pledged by a Broker