A simple analysis of customer impatience in multi-server queues

Choudhury, Amit; Medhi, Pallabi

doi:10.1504/ijams.2011.041319

“…In summary, previous research studies discussed in this regard can be divided into system simulation and comparison of scheduling algorithms ([4]- [6]), the quality of electronic services [7], analyzing and optimizing server model with queuing models ( [8], [9]) and cancellation of the entry and service [10]. Kingman (1961), Koenigsberg (1966), Flatto (1977) and Halfin (1985) studied a model with implementation of queue models in two servers in parallel with unlimited capacity ( [11]- [14]).…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation And Optimization Of Affective Causes On Quality Of Electronic Services

Pirayesh¹,

Heravi²,

Kahani³

2013

J. Math. Computer Sci.

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By expanding bank services and increment in access requests for information resources, web servers perform weak about response time. So it is necessary to use simulation and mathematical models in order to analyzing complicated systems, optimizing and managing web server systems. In this paper, services which are offered through internet are introduced and analyzed as a queue. Web servers are one of the main and effective parts in quality of internet services. Therefore, the operation of web servers is analyzed and optimized by concept of queue and simulation. One of the significant points in this paper is analyzing a problem taken from real world in internet field and also performing a new analysis from user's requests in web servers. The main purpose in this paper is to persuade users and answering theme as soon as possible. In this paper, after introducing the problem's structure, simulation and queue concept are used for analyzing, optimizing and managing web servers. Finally, a summery from results of simulation's calculation is declared.

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“…For instance, the first hitting time can be important to analyze the customer churn that has a huge impact on companies, Mahajan et al (2016). Considering the customer impatience in multi-server queues Choudhury and Medhi (2011) and the customer waiting time in the queue Zhao and Gilbert (2015), the first hitting time indicates the moment when the waiting time exceeds a threshold and hence, the customer may leave the queue. Following Lättilä and Hilmola (2012) the forecasting of exceedances of industrial production can be a driving factor for the development of sea ports.…”

Section: Introductionmentioning

confidence: 99%

Clustering and hitting times of threshold exceedances and applications

Markovich

¹

2017

IJDATS

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Abstract:We investigate exceedances of the process over a sufficiently high threshold. The exceedances determine the risk of hazardous events like climate catastrophes, huge insurance claims, the loss and delay in telecommunication networks. Due to dependence such exceedances tend to occur in clusters. The cluster structure of social networks is caused by dependence (social relationships and interests) between nodes and possibly heavy-tailed distributions of the node degrees. A minimal time to reach a large node determines the first hitting time. We derive an asymptotically equivalent distribution and a limit expectation of the first hitting time to exceed the threshold un as the sample size n tends to infinity. The results can be extended to the second and, generally, to the kth (k > 2) hitting times. Applications in large-scale networks such as social, telecommunication and recommender systems are discussed.

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“…For instance, the first hitting time can be important to analyze the customer churn that has a huge impact on companies, Mahajan et al (2016). Considering the customer impatience in multi-server queues Choudhury and Medhi (2011) and the customer waiting time in the queue Zhao and Gilbert (2015), the first hitting time indicates the moment when the waiting time exceeds a threshold and hence, the customer may leave the queue. Following Lättilä and Hilmola (2012) the forecasting of exceedances of industrial production can be a driving factor for the development of sea ports.…”

Section: Introductionmentioning

confidence: 99%

Clustering and hitting times of threshold exceedances and applications

Markovich

¹

2017

IJDATS

3

0

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Abstract:We investigate exceedances of the process over a sufficiently high threshold. The exceedances determine the risk of hazardous events like climate catastrophes, huge insurance claims, the loss and delay in telecommunication networks. Due to dependence such exceedances tend to occur in clusters. The cluster structure of social networks is caused by dependence (social relationships and interests) between nodes and possibly heavy-tailed distributions of the node degrees. A minimal time to reach a large node determines the first hitting time. We derive an asymptotically equivalent distribution and a limit expectation of the first hitting time to exceed the threshold un as the sample size n tends to infinity. The results can be extended to the second and, generally, to the kth (k > 2) hitting times. Applications in large-scale networks such as social, telecommunication and recommender systems are discussed.

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A simple analysis of customer impatience in multi-server queues

Cited by 5 publications

References 18 publications

Simulation And Optimization Of Affective Causes On Quality Of Electronic Services

Simulation And Optimization Of Affective Causes On Quality Of Electronic Services

Clustering and hitting times of threshold exceedances and applications

Clustering and hitting times of threshold exceedances and applications

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