Conceptualization of Finite Capacity Single-Server Queuing Model with Triangular, Trapezoidal and Hexagonal Fuzzy Numbers Using α-Cuts

2020 ◽  
pp. 201-212
Author(s):  
K. Usha Prameela ◽  
Pavan Kumar
Keyword(s):  
Fuzzy Numbers ◽  
Single Server ◽  
Finite Capacity ◽  
Queuing Model

scholarly journals Applications of Queuing Theory in Quantitative Business Analysis

2020 ◽  
pp. 253-257
Keyword(s):  
Quantitative Analysis ◽  
Waiting Time ◽  
Queuing Theory ◽  
Single Channel ◽  
Analysis Approach ◽  
Single Server ◽  
Expected Number ◽  
Queuing Model ◽  
Infinite Population ◽  
Expected Waiting Time

Queuing Theory provides the system of applications in many sectors in life cycle. Queuing Structure and basic components determination is computed in queuing model simulation process. Distributions in Queuing Model can be extracted in quantitative analysis approach. Differences in Queuing Model Queue discipline, Single and Multiple service station with finite and infinite population is described in Quantitative analysis process. Basic expansions of probability density function, Expected waiting time in queue, Expected length of Queue, Expected size of system, probability of server being busy, and probability of system being empty conditions can be evaluated in this quantitative analysis approach. Probability of waiting ‘t’ minutes or more in queue and Expected number of customer served per busy period, Expected waiting time in System are also computed during the Analysis method. Single channel model with infinite population is used as most common case of queuing problems which involves the single channel or single server waiting line. Single Server model with finite population in test statistics provides the Relationships used in various applications like Expected time a customer spends in the system, Expected waiting time of a customer in the queue, Probability that there are n customers in the system objective case, Expected number of customers in the system

Multi-threshold control by a single-server queuing model with a service rate depending on the amount of harvested energy

2018 ◽  
Vol 127-128 ◽  
pp. 1-20 ◽  
Author(s):  
Chesoong Kim ◽  
Sergei Dudin ◽  
Alexander Dudin ◽  
Konstantin Samouylov
Keyword(s):  
Service Rate ◽  
Single Server ◽  
Queuing Model ◽  
Threshold Control

Transient and steady-state analysis of a queuing system having customers’ impatience with threshold

2019 ◽  
Vol 53 (5) ◽  
pp. 1861-1876 ◽  
Author(s):  
Sapana Sharma ◽  
Rakesh Kumar ◽  
Sherif Ibrahim Ammar
Keyword(s):  
Steady State ◽  
Probability Generating Function ◽  
Threshold Value ◽  
Steady State Solution ◽  
Function Technique ◽  
Single Server ◽  
Queuing Model ◽  
Queuing Models ◽  
Special Cases ◽  
Number Of Customers

In many practical queuing situations reneging and balking can only occur if the number of customers in the system is greater than a certain threshold value. Therefore, in this paper we study a single server Markovian queuing model having customers’ impatience (balking and reneging) with threshold, and retention of reneging customers. The transient analysis of the model is performed by using probability generating function technique. The expressions for the mean and variance of the number of customers in the system are obtained and a numerical example is also provided. Further the steady-state solution of the model is obtained. Finally, some important queuing models are derived as the special cases of this model.

ON PARALLEL QUEUING WITH RANDOM SERVER CONNECTIVITY AND ROUTING CONSTRAINTS

2002 ◽  
Vol 16 (2) ◽  
pp. 185-203 ◽  
Author(s):  
Nicholas Bambos ◽  
George Michailidis
Keyword(s):  
Classical Model ◽  
Single Server ◽  
Job Flows ◽  
Queuing Model ◽  
Finite Buffers ◽  
Parallel Queues ◽  
The Third ◽  
Buffer Overflows ◽  
Random Connectivity ◽  
Application Scope

We study systems of parallel queues with finite buffers, a single server with random connectivity to each queue, and arriving job flows with random or class-dependent accessibility to the queues. Only currently connected queues may receive (preemptive) service at any given time, whereas an arriving job can only join one of its accessible queues. Using the coupling method, we study three key models, progressively building from simpler to more complicated structures.In the first model, there are only random server connectivities. It is shown that allocating the server to the Connected queue with the Fewest Empty Spaces (C-FES) stochastically minimizes the number of lost jobs due to buffer overflows, under conditions of independence and symmetry.In the second model, we additionally consider random accessibility of queues by arriving jobs. It is shown that allocating the server to the C-FES and routing each arriving job to the currently Accessible queue with the Most Empty Spaces (C-FES/A-MES) minimizes the loss flow stochastically, under similar assumptions.In the third model (addressing a target application), we consider multiple classes of arriving job flows, each allowed access to a deterministic subset of the queues. Under analogous assumptions, it is again shown that the C-FES/A-MES policy minimizes the loss flow stochastically.The random connectivity/accessibility aspect enhances significantly the structure and application scope of the classical parallel queuing model. On the other hand, it introduces essential additional dynamics and considerable complications. It is interesting that a simple policy like FES/MES, known to be optimal for the classical model, extends to the C-FES/A-MES in our case.

scholarly journals Two parallel finite queues with simultaneous services and Markovian arrivals

1997 ◽  
Vol 10 (4) ◽  
pp. 383-405 ◽  
Author(s):  
S. R. Chakravarthy ◽  
S. Thiagarajan
Keyword(s):  
Markov Process ◽  
Performance Measures ◽  
System Performance ◽  
Arrival Process ◽  
Single Server ◽  
Queueing Model ◽  
Finite Capacity ◽  
Numerical Examples ◽  
Finite Queues ◽  
Large State Space

In this paper, we consider a finite capacity single server queueing model with two buffers, A and B, of sizes K and N respectively. Messages arrive one at a time according to a Markovian arrival process. Messages that arrive at buffer A are of a different type from the messages that arrive at buffer B. Messages are processed according to the following rules: 1. When buffer A(B) has a message and buffer B(A) is empty, then one message from A(B) is processed by the server. 2. When both buffers, A and B, have messages, then two messages, one from A and one from B, are processed simultaneously by the server. The service times are assumed to be exponentially distributed with parameters that may depend on the type of service. This queueing model is studied as a Markov process with a large state space and efficient algorithmic procedures for computing various system performance measures are given. Some numerical examples are discussed.

A solvable model for a finite-capacity queueing system

1985 ◽  
Vol 22 (4) ◽  
pp. 903-911 ◽  
Author(s):  
V. Giorno ◽  
C. Negri ◽  
A. G. Nobile
Keyword(s):  
Queueing System ◽  
Waiting Times ◽  
Queueing Systems ◽  
Solvable Model ◽  
Probability Density Functions ◽  
Single Server ◽  
Finite Capacity ◽  
System Service ◽  
Transient Probabilities ◽  
Number Of Customers

Single–server–single-queue–FIFO-discipline queueing systems are considered in which at most a finite number of customers N can be present in the system. Service and arrival rates are taken to be dependent upon that state of the system. Interarrival intervals, service intervals, waiting times and busy periods are studied, and the results obtained are used to investigate the features of a special queueing model characterized by parameters (λ (Ν –n), μn). This model retains the qualitative features of the C-model proposed by Conolly [2] and Chan and Conolly [1]. However, quite unlike the latter, it also leads to closed-form expressions for the transient probabilities, the interarrival and service probability density functions and their moments, as well as the effective interarrival and service densities and their moments. Finally, some computational results are given to compare the model discussed in this paper with the C-model.

scholarly journals Transient performance analysis of a single server queuing model with retention of reneging customers

2018 ◽  
Vol 28 (3) ◽  
pp. 315-331 ◽  
Author(s):  
Rakesh Kumar ◽  
Sapana Sharma
Keyword(s):  
Performance Analysis ◽  
Probability Generating Function ◽  
Time Dependent ◽  
Function Technique ◽  
Single Server ◽  
Transient Solution ◽  
Transient Performance ◽  
Queuing Model ◽  
Special Cases ◽  
Mean And Variance

In this paper, we study a single server queuing model with retention of reneging customers. The transient solution of the model is derived using probability generating function technique. The time-dependent mean and variance of the model are also obtained. Some important special cases of the model are derived and discussed. Finally, based on the numerical example, the transient performance analysis of the model is performed.

scholarly journals Dynamic Resource Scheduling Cloud using Enhanced Queuing Model

2020 ◽  
Vol 9 (1) ◽  
pp. 2064-2071
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Cloud Provider ◽  
Single Server ◽  
Queuing Model ◽  
Proposed Model ◽  
Multiple Servers ◽  
Resource Requirements ◽  
Dynamic Resource ◽  
Lock In

The important goal of cloud computing is to offer larger data center that satisfies the storage requirements of the customer. The entire data can’t be saved in a single server. Cloud provider (CP) has cluster of servers to fulfill the cloud request from various real time applications. The data is fragmented in multiple servers to maintain availability. Since the data request of a customer needs data from various servers, there is a possibility of attaining dead lock. In this paper, an enhanced queuing model is proposed where the cloud request (CR) is received in queuing manner for allocation of resources. A session is created for the CR with the CP resource allocation from cloud severs. This enables to put constraint on the number of CR making a session with CP to avoid resource suppression. The Wait for Resource algorithm is used for allocating the server resources to a CR without deadlock in a session. This enables to forecast the resource requirements prior to resource allocation phase in a session. This makes the dynamic resource allocation efficient and free of deadlock. The results obtained evaluates the proposed model and helps the CP in dynamically choosing the number of server nodes necessary to achieve better performance for an real time application.

A finite capacity dynamic priority queuing model

1992 ◽  
Vol 22 (4) ◽  
pp. 369-385 ◽  
Author(s):  
S. Chakravarthy
Keyword(s):  
Finite Capacity ◽  
Queuing Model ◽  
Dynamic Priority
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