Bulk service queue with server breakdown and repairs

Introduction

In an era of technological development, performance modeling is one of the major concerns that impact the design, development, configuration and modification of any real time system. Queueing modeling is used in a wide variety of congestion problems encountered in everyday and in industrial scenario including computer systems, call centers, web services and communication networks, waiting lines at airports, banks, public offices, etc,. Countless queueing models have been developed for nearly a century to examine the performance of many systems and recommendations have been made to suggest that how to deal with congestion situations. In several queueing models, the server may take vacation from the primary job when there is no job present in the system and provides the service for the secondary job; such vacation is known as working vacation. Queueing models with working vacations can be described and defined easily by taking an example of 'an international company' wherein the main task of the receptionist is to answer and divert the calls. Rest of the time, the receptionist may call the potential customers for promoting the company's products as an additional job. Queueing models with vacation have been the subject of interest and interrogation for many years for the queue researchers. Servi and Finn (2002) [23] examined a classical vacation queueing model in which a single server works with a different rate rather than completely stopping service during the vacation period. Gupta and Sikdar (2004) [12] have analysed a single server finite-buffer bulk-service queueing model with single vacation in which the interarrival and service times are considered to be exponentially and arbitrarily distributed, respectively. Madan and Al-Rawwash (2005) [18] have analysed a single server queue with batch arrivals and general service time distribution. Wu and Takagi (2006) [28] have studied an M/G/1 queue with multiple vacations and exhaustive service discipline such that the server works with different rate rather than completely stopping the service during vacation. A vacation queueing model has been examined by Arumuganathan and Jeyakumar (2005) [2] , Palaniammal et al. (2006) [22] and Zadesh and Shahkar (2008) [29] , in different frame works. Ke (2008) [16] has investigated an M X /G/1 queueing system under a variant vacation policy, where the server takes at most j vacations. Omey and Gulck (2008) [21] have studied maximum entropy of the M X /M/1 queueing model with multiple vacations and server breakdowns. Ke and Chang (2009) [15] have analysed the M X /(G1, G2)/1 retrial queue with general retrial times, where the server provides two phases of heterogeneous service to all customers under Bernoulli vacation schedules. Goswami and Selvaraju (2010) [11] have considered a discretetime single-server queueing model with multiple working vacations. Baba (2012) [4] has studied a batch arrival M X /M/1 queue with multiple working vacation and obtained probability generating function and stochastic decomposition structure of the system and some performance indices, mean system length and the mean waiting time. Vijaya Laxmi et al (2013) [25] Analysed a finite buffer renewal input single working vacation and vacation interruption queue with state dependent vacations. They also presented an efficient computation algorithm and computed the stationary queue length for the above model along with different performance measures. Sree Parimala and Palaniammal (2014) [24] studied bulk service queueing model with server's single and delayed vacation. For this model, the steady state solutions and the system characteristics are derived and analysed. Ibe (2015) [13] has considered a single server vacation queueing system with server time out and derived expressions for the mean waiting time and studied N-policy scheme. Bharathidass (2013) [8] has analysed a Markovian bulk service queue under accessibility rules in which a single server Markovian queue is considered. The arriving units served in batches by using Accessibility and Non-Accessibility with varying service rates. The expressions for the steady state probabilities when the server is busy as well as idle are derived. The mean and variance for the number of units in the queue are obtained. The expected waiting time of units is also attained. Also the numerical results for number of units in the queue are computed for various value of arrival rate and exhibited the corresponding graphs when the remaining parameters are fixed. Balasubramanian et al (2015) [8] have analysed a finite size single server Markovian queue with restoration, catastrophe and general bulk service. The expression for the expected number of times the system reaches to the capacity, expected number of customers in the system and respective factorial moments have been derived under different conditions. Balasubramanian et al (2016) [7] have studied a Markovian queue with blocking and catastrophes in which a queueing system with two servers is considered and the arrival times of units and service times corresponding to both servers follow Markovian nature. The expressions for the expected queue length in the system and loss probability of units have been derived and also the numerical results are provided. Arivukkarasu et al (2016) [1] have studied customer impatience in bulk arrival queue with optional service in which considered a single server queue and customers arrive in batches of different sizes. The arrival times of batches follow Poisson process and the service times of essential and optional services are distributed according to exponential and general distributions respectively. The principles of customer's impatience, server's vacation and bulk size rule have been employed. The expressions for the probability generating functions, steady state probabilities and expected system size have been derived when the server is on vacation, busy with essential service and busy with optional service. Vacation queueing systems with service interruption have also been investigated in the recent years by researchers. No system is found perfect in the real world since all the devices fail more or less frequently. It is an inevitable phenomenon which can be realized in computer, communication and manufacturing systems. Several day to day queueing problems on industry and society have been studied by using queueing concepts under the assumptions of server vacation and unreliable server. Many researchers have analyzed single server queueing system with service interruption. Other denominations of an unreliable server queueing model have been analysed by a list of queueing experts like Wang (2004) [26] , Choudhury (2009) [9] , Wang and Haung (2009) [27] and Jain and Jain (2010) [14] . Maraghi et al (2009) [20] have studied a batch arrival queueing system with random breakdowns and Bernoulli schedule server vacations having general vacation time distribution and obtained the steady state results for queue size, mean queue size and mean waiting time of the customer in the queue along with some special cases discussed and some known results have been derived. Khalaf et al (2011) [23] have discussed an M [X] /G/1 queueing system with Bernoulli schedule server vacations and random system breakdowns with general delay and general repair times. The steady state solutions have been found by using supplementary variables technique. Ayyappan and Shyamala (2013) [3] have considered a queueing model with batch arrival with second optional service, Bernoulli ssssssssssk-optional vacation and balking. The time dependent solution and the corresponding steady state solutions have been derived along with performance measures, the mean queue size and the average waiting time explicitly. Balamani (2014) [5] has studied a two stage batch arrival queue with compulsory server vacation and second optional repair and has derived the steady state solutions also computed the mean queue length and the mean waiting time. Ebenesar Anna Bagyam et al (2015) [10] have considered bulk arrival multi-stage retrial queue with Bernoulli vacation, the steady state distributions of the server state and the queue size have been obtained along with important measures. Maragathasundari and Karthikeyan (2015) [19] have studied about a Non Markovian queueing model with extended server vacation, random breakdowns and general repair. Also derived the steady state results in explicit and closed form in terms of the probability generating functions for the number of customers in the queue, average number of customers and mean waiting time in the queue. In real life situations, the server may fail and require repair. The interruption of service to the customers makes negative impact on the performance of any system. In this kind of situation, there is a basic need to develop a reliable queueing system for maintaining the better performance of service. The multi optional repair concept is more realistic to model the unreliable server queue. When the server is not restored with the first essential repair, then the provision of subsequent repairs may be helpful for restoring the server; this phenomenon is known as 'multi optional repair'. In this paper, the unreliable M/Ek/1 queue using the multi optional repair has been studied. The concepts of working vacation along with service interruption due to the server breakdown incorporated make our queueing model more robust and versatile. The queueing analysis carried out by using an analytical method and multi-optional repair based on generating function. The rest of the paper is organized as follows. Section two describes the model by stating the necessary assumptions and notations which are needed to develop the model. The steady state equations governing the model are constructed. In Section three, the steady state equations governing the model are constructed. In section four, the different partial probability generating functions are defined. Section five is dedicated to estimating the probability generating functions. In section six, state probabilities are obtained for different positions of the server. Also in section seven, average number of units in the system is derived based on the positions of the server.

Probability Generating Functions

In this section, the different partial probability generating functions are defined when the server is on working vacation, busy, break down under first essential repair and j th 2 phase of optional repair respectively.

Here , , , and are the corresponding probability generating functions in the i th service phase.

Estimation of Probability Generating Functions

This section is devoted to obtain various probability generating functions. Initially, by using steady equations and stated probability generating functions, the probability generating functions at i th service phase and the over all probability generating functions are derived when the server is on different levels. The equations ( 2) and ( 4

Follow the above technique in equations ( 6) and ( 8) and get

; By adding the equations ( 7) and ( 9) after multiplying them by z b+1 and z b+n , (n = 2, 3, 4,…) respectively, and get

The equations ( 10) and ( 11) are multiplied by z and z n respectively and summing over n = 1, 2,…. Then,

The equations ( 12), ( 14), ( 15) and ( 16) are multiplied by z and both ( 13) and ( 17) are multiplied by z n (n = 2, 3, …). The sum of the above equations is obtained as

In this stage, using the method of iteration in the equation (23) and get ; 1,2, … , 1

Substitute (29) in the expression (24) which yields

References

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