Retrial Queueing Models: A Survey on Theory andApplications
Tuan Phung-Duc
Faculty of Engineering, Information and Systems
University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Email: tuan@sk.tsukuba.ac.jp
Abstract
Retrial phenomenon naturally arises in various systems such as call centers, cellular networks and random access protocols in local area networks. This paper gives acomprehensive survey on theory and applications of retrial queues in these systems.
We investigate the state of the art of the theoretical researches including exact solutions, stability, asymptotic analyses and multidimensional models. We present an
overview on retrial models arising from real world applications. Some open problems
and promising research directions are also discussed.

1

Introduction

The loss models (including Erlang loss model) assume that an arriving customer that sees
the service area being fully occupied is blocked and is lost forever. On the other hand, inmodels with an infinite waiting capacity, a customer waits until being served. However,there are various situations in our everyday life and in various systems where blocked
customers are not willing to wait and they temporarily leave the service facility for a while
but try again after some random time. A blocked customer is said to be in a virtual waiting
room called orbit before retrying to occupy a server again. These situations are modeled as
retrial queues.
For example, in a call center, if a customer makes a phone call when all the agentsare busy, the customer will try to make a phone call again after some random time. In
computer networks, if a packet is lost, the packet may be retransmitted at a later timeby a retransmission mechanism such as the TCP (Transmission Control Protocol) [24, 25].
In these applications, the orbit is virtual and cannot be observed. Figure 1 represents ageneral multiserver retrial queue.
1

In many applications, the customers in the orbit act independently of each other, thus
the retrial rate depends on the number of customers in the orbit. As a result, the underlying
Markov chains of retrial queues have a spatially non-homogeneous structure. Due to thespatial non-homogeneity of the underlying Markov chains, the analysis of retrial queues ismore complex and challenging than that of standard queues. For an extensive comparisonof standard and retrial queueing systems, the readers are referred to the paper of Artalejo
and Falin [15]. Even for the M/M/c/c retrial queue, where retrial interval of customers
follows an exponential distribution, analytical solutions are obtained in only a few special
cases [15, 57].
Service Area
Servers
Orbit
Waiting
room

Yes
No

Systemis full?

Figure 1: A general multiserver retrial queue.

Classical retrial policy
In a retrial queue with the classical retrial policy, each blocked customer stays in the orbit
for an exponentially distributed time independently of other customers. As a result, theretrial rate is proportional to the number of customers in the orbit. The classical retrial
policy naturally arises from applications such as call center and telephone exchange system,
where customers in the orbit act independently because a retrial customer cannot observe
the behavior of the others. Retrial queues with the classical retrial policy have attracted
many researchers since they naturally arise from various applications with random access.
Under some Markovian settings of the service time distribution and the arrival process, theunderlying stochastic process is a level-dependent quasi-birth-and-death (LDQBD) process
where the level is the number of customers in the orbit and the phase represents the statesof the servers. For general LDQBD processes, some general numerical algorithms are avail-2

able [31, 99]. The sparsity of the block matrices of the LDQBD processes of retrial queues
could be used to develop efficient computational algorithm [101].

Constant retrial policy
There are several applications in communication networks, where the retrial of customers
is controlled. It means that the retrial rate may not depend on the number of customers
in the orbit. For example, Choi et al. [35] study the stability of the CSMA/CD (Carrier
Sense Multiple Access with Collision Detection) protocol, by a retrial queue with a constantretrial rate. Avrachenkov and Yechiali [24, 25] use a retrial queueing network with constantretrial rate to model TCP traffic. Constant retrial rate could be interpreted by the so-called“calling for blocked customers”. When the server is idle, it calls blocked customers one byone. The time for the server to pick up a blocked customer could be interpreted as theretrial time.
Artalejo et al. [12] formulate a multiserver retrial queue (M/M/c/c) with constant retrial
rate by a level-independent QBD process which could be analyzed efficiently using matrix
analytic methods invented by Neuts [87, 93]. As in the classical retrial rate, the block
matrices of the level-independent QBD process are also sparse leading to efficient algorithms
for the stationary distribution. These algorithms are discussed by Artalejo et al. [12] using
a matrix analytic method and by Do et al. [] using the spectral expansion method [44, 45].
The structure and aim of this chapter are as follows. First in Section 2, we provide acomprehensive review of retrial queues in real world applications. Second, in Section 3, we
present the main results on the analysis of retrial queueing models. The aim of this survey
is to provide a guide for researches who want to enter and deepen the understanding of
the field of retrial queues. To this end, we point out some open problems and promisingresearch topics.

2

Retrial Queues in Applications

In this section, we present several retrial queueing models arising from real world applications.

3

2.1

Call Centers

A call center is important for a company because it provides a channel for customers tocontact the company. In a call center, agents are the people who answer the calls from thecustomers. When a customer makes a phone call, if there is an idle call agent, the customer
is immediately answered by the call agent. If all the agents are busy, the customer may
hear some massage such as “the system is busy at the moment, please wait for a moment”.
At this moment, the customer either hangs up the phone immediately or continues to hear
the message. In the former case, the customer may try again after some time. Customers
who decide to wait for a free call agent may renege if the waiting time is too long. These
customers may also make a phone call later.
 !"#$

012345678%&'()*+ ,-./  

 Figure 2: A retrial queueing model for call centers.

One of the most important performance measures for a call center is the blocking probability that a customer cannot find a free call agent upon arrival. From the customer’s pointof view, a low blocking probability is desirable. In order to keep the blocking probability to
be low, a simple solution is to increase the number of agents. However, from a management
point of view we need to minimize the number of agents, due to the fact that the cost ofa call center is mainly the human cost [121]. In order to achieve the customer satisfaction
under some constraint on the cost, we need some mathematical model to express the tradeoff between the customer satisfaction and the human cost. A queueing model is one of themost appropriate mathematical models for the design of call centers. In addition, in orderto capture the retrial phenomenon as presented above, a retrial queueing model is expected
to be more appropriate than the corresponding standard queueing model [62, 81, 121, 1].
See Figure 2 for a simple retrial queueing model for call centers. For a detailed explanation
on call centers, we refer to the book by Stolletz [121]. Numerical results by Phung-Duc et4

al. [101] show that approximating retrial flow by a Poisson process leads to a large error
under the fast retrial regime. This is an evidence for modelling call centers by retrial queues.
Phung-Duc and Kawanishi [104] consider two-way communication retrial queueing systems as the models for blended call centers. Queueing models without retrials for blended
call centers are proposed and analyzed in [32]. Two-way communication retrial queues
are studied by Artalejo and Phung-Duc in [21, 22] where some analytical results such as
the stability condition and generating functions are obtained. The main contribution in
Phung-Duc and Kawanishi [104] is to propose an efficient computational algorithm for
multiserver retrial queues with distinct distributions of incoming and outgoing calls. The
algorithm [104] could be considered as a matrix version of the one by Phung-Duc et al. [101].
In two-way communication queueing systems, a server not only receives incoming calls but
also makes outgoing calls to outside in its idle time. This is the situation in blended callcenters where the operators may make outgoing calls to the customers for some marketing
purposes etc.
Furthermore, Phung-Duc and Kawanishi [105] analyze a fairly general and practical
retrial model for inbound call centers with after-call work and abandonment. After-callwork is a typical task in call centers where after a conversation with a customer, theoperator should do some after-call work for that customer after the customer departs fromthe system. It means that a call line is released for a newly arrived customer. In [105], theeffects of retrials by blocked and abandoned customers on the waiting time distribution areinvestigated.

2.2

Cellular Communication Networks

In a cellular network, the service area is divided into cells. Users (mobile stations) in eachcell are served by a base station with a limited number of channels. Therefore, only alimited number of users can communicate at the same time. A base station serves two
types of calls: fresh calls and handover calls. A fresh call is made by a user that stays
inside the current cell and a handover call is made by a user that has been traveling froman adjacent cell into the current cell. Because a handover call has been communicating bya channel in an adjacent cell, the call should be assigned a channel upon its arrival into thecurrent cell as soon as possible for a continuous communication. Therefore, a handover call5should be given a priority over a fresh call.
9:;<= >?@A BCDE]^_`abcd efgh
QRSTUVWX YZ[\

FGHIJKL MNOP

Figure 3: Two adjacent cells in cellular networks.

There are several channel assignment policies that provide some priority for handovercalls over fresh calls [45, 123]. In a guard channel policy, a number of channels are reservedfor handover calls. The rest of channels are equally shared by both fresh and handover calls.
In a fractional guard channel policy, fresh calls are accepted with a probability dependingon the number of currently occupied channels, while handover calls are accepted as longas an idle channel is available [114]. Both fresh calls and handover calls may be blocked
due to the limit on the number of channels. In modern cellular communication systems(e.g. mobile phone system), if a call is blocked, a redial can be made easily, for example
just by pushing only one button. In some applications, blocked calls are automatically
redialed. Thus, the consideration of retrial calls is very important while designing thesesystems [123].
Figure 3 represents two adjacent cells in a cellular networks. In Figure 4, a buffer for
handover calls represents overlap areas of the current cell with the adjacent cells. Handovercalls in the overlap areas can receive signals from both the adjacent cell and the current cell.
Thus, if a channel in the current cell is not yet available, the handover call can continue
to communicate using the occupying channel in the adjacent cell. However, the handovercall is terminated if it exceeds an overlap area but no idle channel in the targeting cell isavailable. In this case, the handover call may attempt again after some random time as a
fresh call.
In addition to the pure Markovian models presented in [45, 123], more general models
with correlated arrival processes such as Markovian Arrival Processes (MAP) and Batch
Markovian Arrival Processes (BMAP) and phase type service time distributions are investi6

gated in [2, 37, 74, 75]. A model under random environment is presented in [20]. In all thesemodels, the performance measures are directly calculated from the stationary probabilities.
Economou and Lopez-Herrero [52] provide some more sophisticated performance measures
such as waiting time distribution, idle time of guard channels etc.

No
Fresh calls

Yes

Accept?

Handover calls

Figure 4: A model of cellular networks with overlapping cells.

2.3

Local Area Networks

In a local area network (LAN), multiple nodes share a physical link (channel) in order totransmit their data (packets). Assuming that multiple nodes send their packets at the sametime, a collision may occur and all the packets will be destroyed.
Because nodes in a LAN are located closely to each other, propagation delays are much
shorter than data transmission time. Therefore nodes can obtain useful information about
the channel by sensing the presence of signals on the channel. Taking this property into
account, CSMA (Carrier Sense Multiple Access) protocols have been developed. The fundamental idea of a CSMA protocol is that it senses the channel before transmitting data.
In a 1-persistent CSMA protocol, when a node is ready to send its data, the node checks if
the channel is busy. If the channel is busy, then the node continues to sense the link until
the channel is idle and then immediately sends a frame. In case of collision, the node waits
for a random period of time and tries to transmit again. The problem of the 1-persistent
CSMA protocol is that in a highly loaded condition, there may be several nodes waiting
for the availability of the channel and they send data at the same time when the channel
becomes idle. Therefore, collisions occur with a high probability.
Another protocol is non-persistent CSMA, in which if a node is ready to send data, it
senses the channel and transmits data immediately if the link is available, otherwise thenode waits for a random time and tries to retransmit again. A p-persistent CSMA protocol
7

is considered to be a hybrid of 1-persistent CSMA and non-persistent CSMA protocols. In
a p-persistent CSMA, if the channel is idle, the node transmits a frame with probability p
and delays its transmission for one unit time (channel propagation delay) with probability
1 − p. If the channel is busy, the node continues to sense until the channel becomes idle
and repeat the same procedure to send its frame.
Because the collisions in CSMA protocols cannot be completely avoided, the retrial phenomenon occurs in local area networks, e.g. wireless networks. Therefore, retrial queueing
models are considered to be more appropriate than standard queueing models in modellingand performance analysis of these protocols [35, 67]. The readers are referred to the bookby Kurose and Ross for a detailed explanation on protocols for local area networks [83].
Because the behaviors of nodes in radio networks depend on each other, analysis of an
exact model representing the states of all nodes is very challenging. Assuming that the
behaviors of nodes are independent, Bianchi [28] demonstrates a simple analysis of thiscomplex system. Recently, Fiems and Phung-Duc [60] propose a light traffic analysis by aseries expansion method for a retrial model which represents all the states of nodes concurrently. The accuracy of the model is validated by simulations. The series expansion
method [60] might be useful for other retrial models for local area networks with random
access protocols.

2.4

Retrial Queues for Cognitive Networks

Recently, cognitive radio networks are extensively studied. The spectrum for wireless networks is physically limited. On the other hand, the amount of traffic by smart phones
and other devices vastly increases day by day. However, most of bandwidth is granted tolicensed users (primary users). The bandwidth is not always used by primary users. The
idea of cognitive networks is to provide the opportunity for secondary users to use thisbandwidth when it is not used by the primary users. Secondary users are interrupted upon
the arrivals of primary users. Queueing analysis of cognitive networks has been presented
by Konishi et al. [80] using a multiserver priority model without buffer where interrupted
secondary users are lost. In practice, interrupted users may retry again. From this point ofview, appropriate retrial queueing models might be useful for cognitive systems. Wang etal. [127, 128] use some simple M/M/1/1 retrial queueing models to study strategic behav-

8ior of secondary users in cognitive networks. Dudin et al. [50] propose a multiclass retrial
queueing model for cognitive systems. Salameh et al. [124]study retrial queueing models
taking into account the sensing time of interrupted secondary users. Because cognitive radio is promising solution for the insufficiency of wireless spectrum [80], new retrial models
for various scenarios and technologies in cognitive systems are promising topics for future
researches.

2.5

Retrial Queues in other Applications

Beside concrete applications mentioned in previous subsections, retrial queues are alsoubiquitous in other applications. Retrial queues for optical networks are presented in [96,61, 29]. Furthermore, some emerging technology such as cloud computing, one can also findvarious situations where retrial queues are applicable. For example, single server retrial
queues with setup time are proposed for power-saving servers [107, 110]. Phung-Duc and
Kawanishi [113] investigate the impact of retrial phenomenon on power saving data centersby an M/M/c/c retrial queue with setup time. In cloud systems, the computing unit andthe storage unit may be separated leading to the transmission time between them. A
retrial model for such a situation is initiated in [109]. Furthermore, in cloud systems, thecapacity of the server can be scaled according to the workload in the system. Taking this
into account, Phung-Duc et al. [108, 111] propose single server retrial queueing models withspeed scaling and setup time where the speed of the server is proportional to the numberof jobs in the system.

3

Models and Methodologies

In the analysis of retrial queueing models, we need to keep track not only of the state of
the servers but also the number of customers in the orbit. Under the assumption that
retrial customers behave independently, the retrial flow by repeated customers makes theunderlying stochastic process non-homogeneous. As a result, the analysis of retrial queues
is more difficult than the corresponding models with infinite waiting room. Indeed, themodel with infinite waiting room could be obtained from the corresponding retrial models
by taking the limit as the retrial time tends to zero. In this section, we show methods andanalytical results for some major retrial queueing models.
93.1

M/G/1/1 Retrial Queues and Their Variants

In this model, there is only one server and there is not a waiting room before the server.
Customers arrive at the system according to a Poisson process and the service time fora customer is arbitrarily and identically distributed. An arriving customer immediately
occupies the server if it is idle. Otherwise, the customer will join the orbit from which hewill retry again in an exponentially distributed time with positive mean. This model could
be analyzed by either embedded Markov chain or by complementary variable method as
for the original M/G/1 model without retrials.
Let πi,n denote the stationary probability that the server is at state i (i = 0 if the serveris idle and i = 1 if the server is busy) and there are n customers in the orbit. Furthermore,Pnlet Πi (z) = ∞n=0 πi,n z (i = 0, 1). For this model, the generating functions, i.e., Πi (z)(i = 0, 1) of the number of customers in the orbit can be obtained in an integral form [57].
Furthermore, π0,0 is given in an integral form and other probabilities πi,j are recursively
computed. If the service time is exponentially distributed, the integral forms of Πi (z)(i = 0, 1) become explicit. Furthermore, under the light-tailed assumption of the service
time, asymptotic formula for the joint queue length distribution is known. In particular,
π0,n ≍ C0 na−1 σ −n and π1,n ≍ C1 na σ −n as n → ∞ [78] for some positive constants C0 , C1 ,a and σ > 1.
Artalejo and Phung-Duc [22] study M/G/1/1 retrial queue with two-way communication where the server makes an outgoing call in an exponentially distributed idle time withmean 1/α. In [22], incoming calls and outgoing calls follow two distinct arbitrary distributions. Under light-tailed assumption of the service time distributions of incoming andoutgoing calls, asymptotic analysis of the joint queue length is also presented in [22] using
a simpler method in comparison with that of Kim et al. [78] for the M/G/1/1 retrial queue
without outgoing calls. Under some heavy tailed assumptions of the service times, thequeue length asymptotics is presented in Shang et al. [116] for the M/G/1/1 model and by
Yamamuro [129] for M X /G/1/1 retrial queue.
Heavy traffic asymptotics is presented in Falin [57]. In particular, when the traffic
intensity is close to one, the scaled queue length distribution tends to Gamma distribution.
Furthermore, when the retrial rate is extremely low, the distribution of the scaled numberof customers in the orbit tends to Gaussian distribution. Sakurai and Phung-Duc [115]10study heavy traffic analysis for M/G/1/1 retrial queue with two-way communication. In
addition to the two heavy traffic regimes: i) traffic intensity is close to 1 and ii) extremely
slow retrial rate, the authors also study the regime iii) where length of an outgoing callis extremely long. Sakurai and Phung-Duc prove that in regime iii), the distribution forthe scaled number of customers in the orbit tends to Gaussian distribution. We refer torecent work of Nazarov et al. [91] and Fedorova [59] for recent development on heavy traffic
analysis of retrial queues without and with random environment.
An extension of the M/G/1/1 retrial queues with nonpersistent customers is proposedby Yang et al. [131]. In this model, a blocked fresh customer joins the orbit with probability
p while a blocked retrial customer joins the orbit with probability q, respectively. With thecomplementary probability, they abandon joining the orbit. In the case q = 1, the analysis
is almost the same as that of the basic model with p = q = 1. However, the analysis
for the case q < 1 is essentially different from that of the case q = 1. In particular,
although the equations for the partial generating functions of the joint queueing length
distribution are obtained, the solution for these equations is not obtained. It is shownthat all the quantities interest such as the joint stationary joint queue length distribution
and the factorial moments are expressed in terms of the utilization of the server whichis unknown. A numerical algorithm is then developed to determine the utilization of theserver. However, analytical expression for the utilization remains an open problem.

3.2

Multiple Server Models

While the single server model is relatively tractable in comparison with that without retrials,
multiserver retrial queues are much more difficult than those without retrials. The basic
M/M/c/c retrial queues with classical retrial rate (i.e. exponential retrial intervals) has
been studied by Cohen [38]. In this system customers arrive at the system according toa Poisson process with rate λ, the service time of a customer is exponentially distributedwith mean 1/ν and the retrial intervals are exponentially distributed with mean 1/µ.
In this model, let πi,j denote the joint stationary probability that there are i (i =0, 1, . . . , c − 1, c) busy servers and j customers (j ∈ Z+ ) in the orbit. Furthermore, letPjΠi (z) = ∞j=0 πi,j z (i = 0, 1, . . . , c). Explicit expressions for πi,j and Πi (z) are obtained
for the case c = 1. For c = 2, πi,j and Πi (z) are expressed in terms of hypergeometric11

functions [71]. For the case c ≥ 3, it is challenging to obtain analytical solutions for πi,jand Πi (z). Pearce [95] constructs a solution to the balance equations in terms of generalized
continued fraction. Phung-Duc et al. [97] prove for the case c = 3, 4 that πi,j is expressedin terms of the minimal solution of a three-term recurrence relation and thus in terms ofcontinued fractions.
Phung-Duc et al. [98] extend the analysis to M/M/c/c (c ≤ 4) retrial queues with
nonpersistent customers where a blocked fresh customer and a blocked retrial customer
joins the orbit with probability p and q, respectively. As shown in [98], the solution forthe model q = 1 is almost the same as that of the basic model with p = q = 1, while thesolution for the case q < 1 exhibits a different structure. Furthermore, an accurate algorithm
is developed to calculate these continued fractions with pre-specified accuracy [98].
Tail asymptotic analysis for the queue length distribution of M/M/c/c retrial queues has
been extensively investigated. Liu and Zhao [86] show that πc−i,j is of the order of j a−i ρj
as j → ∞ for some constant a where ρ = λ/(cν) < 1, using a matrix analytic method. The
analysis of Liu and Zhao [86] is based on the series expansion (up to second order) of therate matrix Rn of the underlying level-dependent QBD process in terms of 1/n. The series
expansion is extended to any order by Phung-Duc [106] for M/M/c/c models with two typesof non-persistent customers. It should be noted only the last row of Rn is non-zero in thesemodels. Kajiwara and Phung-Duc [72] further extend the analysis of Phung-Duc [106] toan M/M/c/c retrial model with one guard channel for priority and retrial customers where
the last two rows of the rate matrix Rn are non-zero. Kim et al. [76, 77] refine the resultsof [86] by a generating function approach.
A simple and accurate fixed point approximation is proposed by Cohen [38] for the casewhere the retrial rate is relatively small in comparison with the service rate. In such asituation, the total arrival flow by both fresh customers and retrial ones is approximatedby a Poisson process whose rate is the solution of a fixed point equation (See e.g. Falin [57]for details). Let λ + r denote the arrival rate of the approximated Poisson process, wherer is the additional arrival rate due to retrial customers. Let B(λ, c) denote the blocking
probability of the corresponding Erlang-loss system without retrials where ν = 1. The
additional arrival rate r is the solution ofr = (λ + r)B(λ + r, c).
12(1)

The main reason for this approximation is that under low retrial regime, the retrial flow
is likely to form a Poisson process. Recently, the heavy traffic analysis (also known as
Halfin-Whitt regime) for equation (1) is presented by Avram et al. [27].
For a numerical computation of the joint stationary distribution, we need somehow to
truncate the orbit at some truncation point. The simplest truncation method is limiting thenumber of customers in the orbit to N. Thus, a blocked customer that sees N customers
in the orbit is lost. Another truncation method is to modify the structure of the original
Markov chain after the truncation point. The modification makes Markov chain analyticallytractable. This is referred to as generalized truncation in the literature [57, 92, 8, 14]. The
idea in [57, 14] is to disregard the states (after the truncation level (orbit size)) having small
probability mass.
More general models with MAP or BMAP arrivals and phase-type service time are also
investigated [30, 79, 74, 73] by means of the so-called quasi-Toeplitz Markov chains [79].

3.3

Stability Conditions

The stability condition of the basic M/M/c/c retrial queue is simply given by λ < cν or theoffered load (λ/ν) is less than the number of servers. The proof of this result is based on an
appropriate Lyapunov function of a linear form f (i, j) = ai+j, where i is the number of busy
servers and j is the number of customers in the orbit [57]. Artalejo and Phung-Duc [21]derived the necessary and sufficient condition for M/M/c/c retrial queues with two-way
communication where an idle server may make an outgoing call after some exponentiallydistributed idle time with mean 1/α. In [21], incoming calls and outgoing calls follow two
distinct exponential distributions. It turns out that the stability condition of the M/M/c/c
retrial queues with two-way communication coincides with that of the corresponding model
without outgoing calls, i.e., λ < cν. Phung-Duc and Dragieva [112] obtain the stability
condition for a multiserver retrial queue with interaction between servers and orbit where
not only customers retry but also servers call for customers from the orbit. The Lyapunov
function for the models in [21, 112] is of the form f (i, j, k) = ai + bj + k where i and j
are variables representing the states of the servers and k is the number of customers in theorbit.
For retrial models whose LDQBD process has complex phase structure, it is efficient13

to use the Lyapunov function proposed by Diamond and Alfa [41]. This approach has
been used to show the stability condition for MAP arrival models, model with after callwork [103, 105] and model with setup time [113].
In a recent paper, Shin [118] proves that the stability condition for the multiclass
M/M/c/c retrial queue is that the total offered load of all classes is less than the number of servers. The proof of Shin [118] is based on a Lyapunov function that is a linear
combination of the numbers of customers in the orbits of all classes and the states of theservers. The stability condition concerns when there are a very large number of customers
in the orbit. Thus, the time that a retrial customer reaches an idle server is almost zeroin such a situation. This is an intuitive observation that the stability condition of retrial
queues coincides with that of corresponding models with infinite buffer. Recently, Dayarand Can Orhan [39] prove the stability condition for multiclass MAP/PH/c retrial queues
with cyclic PH retrial times. The proof in [39] is based on a Lyapunov function which isalso a linear function of the numbers of customers in the orbit. It should be noted that the
Lyapunov function approach is applied for Markovian models only.
In a fairly general class of retrial queues (both single and multiple class models) withclassical retrial rate, the coincidence in the stability conditions between retrials models andnon-retrial models is confirmed by Morozov et al. [89, 90] for more general non-Markovian
models (i.e., arbitrary renewal arrivals and arbitrary service time) using the regenerative
approach. The regenerative approach of Morozov et al. is also used to prove other models
with constant retrial rate [3, 4]. The result in [90] generalizes that of Shin [118].

3.4

Multiclass Models

3.4.1

Classical retrial policy

We consider the multiclass M/G/1/1 retrial model with classical retrial rate where mclasses of customers arrive at the server according to m distinct Poisson processes with
rate λ1 , λ2 , . . . , λm . The service times of m classes of customers follow m distinct arbitrary
distributions. A blocked customer of class k, joins the k-th orbit and retries to enter theserver after some exponentially distributed time with mean 1/µk (k = 1, 2, . . . , m). For
this model and its variants the stability conditions are available and the means number ofcustomers (also mean waiting time) in the orbit for each class are obtained [57, 55, 82].

14

Grishechkin [70] studies single server retrial queue with structured batch arrivals and investigates some heavy traffic limits for the queue length process. Many open questions forthese models such as heavy traffic for slow retrial case, waiting time distribution are still
open for further investigations. Under multiserver settings, only the stability conditions are
known [89, 90, 118, 39].
3.4.2

Constant retrial policy

Multiclass M/G/1/1 retrial queues with constant retrial rates have been paid much attention in recent years. For this model, under Markovian assumptions, i.e., the service timesof each class follow a distinct exponential distribution; the underlying Markov chain ismulti-dimensional. In the case of two classes, boundary problems are formulated and some
information on the means number of customers of two classes in the orbit may be derived.
Furthermore, information on the joint generating functions of the numbers of customers
in the orbit are obtained [26] using Riemann-Hilbert boundary value problems. Song etal. [120] study the same model using a kernel approach and obtain tail asymptotic for thequeue length of each class. Dimitriou [42, 43] studies some extended models for network
coding in relay nodes in wireless networks.

3.5

Priority and Related Models

3.5.1

Classical retrial policy

In retrial queues with priority, blocked priority customer can wait in front of the server while
blocked normal customer joins the orbit. Under the classical retrial rate setting, bivariate
generating functions for the joint queue length distribution of the number of customers innormal queue and that in the orbit is obtained in an integral form [36, 57, 58]. For thismodel, heavy traffic analysis (the total traffic intensity tends to 1) is carried out by Falin etal. [58]. Recently, Walraevens et al. [125] derive tail asymptotic formulas for the stationary
distribution of the number of customers in the orbit. A detailed survey on related models
is presented in [36].
3.5.2

Constant retrial policy

Priority retrial queues with constant retrial rate are also paid much attention. Lower
priority customer joins the orbit while high priority customer joins the priority queue. The
15

orbit operates as a single server queue where customers retry to occupy the server according
to a FCFS manner and only the customer in the head of the orbit queue retries at a time.
Under a pure Markovian setting, i.e., exponential retrial time and service time, Li and
Zhao [85] obtain tail asymptotics of the priority queue given a fixed number of customers
in the orbit queue. Gomez-Corral [64] considers the model with constant retrial rate where
the retrial time of the customer in the head of the orbit and the service time are arbitrarily
distributed. Atencia and Moreno [23] analyze the model under general retrial time andBernoulli scheduling. In this model [23] an arriving customer that sees the server busy
either joins the priority queue (normal queue) or the orbit queue. The authors [23] obtain
the bivariate generating function of the number of customers in the orbit and that in thenormal queue and the remaining service time or the remaining retrial time. The analysisof the models with both arbitrary service and retrial times is possible because at any time
we need to keep track of either remain service time or remaining retrial time.

3.6

Queueing Networks with Retrials

In our everyday life, there are various service systems, in which customers are served by anumber of servers in a certain order. For example, in a manufacturing system, a product ismade and checked by a number of persons. In a computer system, a message is transmitted
from a source to a destination through several devices such as computers, routers andswitches. These systems can be modeled by queueing networks.
Queueing networks with retrials do not possess product form solution [16]. Thus, exact solution is obtained in a few special cases [102, 88]. Phung-Duc [102] obtains explicit
solution for a simple two-node tandem network with classical retrials at the first node.
Moutzoukis and Langaris [88] derive the explicit results for the tandem model with constant retrial rate and blocking at the first server. For complex retrial queueing networks, apractical approach is the fixed point approximation. For example, fixed point approximations are used to analyze some tandem models with retrials by Avrachenkov et al. [24, 25].
In fixed point approximation, the system is divided into multiple subsystems whose input
parameters are unknown. Furthermore, these subsystems are assumed to be independent.
The output of one subsystem is the input of another subsystem. After some iterative calculations, one will get a convergence determining all the unknown parameters. One drawback16

of this methodology is that a rigorous proof of the convergence and the accuracy of theapproximation are not always presented. Moreover, the approximation is basically valid
only under the slow retrial regime. Numerical solutions for some simple tandem retrial
models are presented in [74, 73, 122, 65].
Recently, Fiems and Phung-Duc [60] present a light-traffic analysis for finite-source
retrial systems arising from CSMA protocols without collision. These systems could beformulated by multidimensional Markov chains which are also used to represent retrial
queueing networks. The analysis by Fiems and Phung-Duc [60] is based on series expansion
subject to the arrival rate around the origin and is validated by simulation. Thus, power
series expansion may be useful for analyzing queueing networks with retrials.

3.7

Model with Orbital Search

Orbital search mechanism for customers in the orbit is introduced by Artalejo et al. [10],where upon service completion, with some probability the server can pick a job fromthe orbit with zero searching time. This mechanism is further extended in [49, 40, 84].
Chakravarthy et al. [33] investigate multiserver models where an idle server immediately
picks up a customer from the orbit (zero searching time) with a probability or stays idle
with the commentary probability. Recently, Dragieva and Phung-Duc [112, 48] propose
a related model that the authors call the retrial queueing model with interaction between
server and customers in the orbit. The main idea is that not only customers retry to capture
an idle server (incoming calls) but the server also makes outgoing calls to retrial customers.
The distributions of the durations of incoming calls and outgoing calls are different. Under
M/M/1/1 settings, the authors in [48] obtain explicit solution for the generating functions of
the joint queue length distribution and the stability condition is obtained for the M/M/c/cmodel.

3.8

Game Theoretic Analysis

Recently, game theoretic analysis of queues has been attracted much attention. Someauthors study game theoretic analysis for retrial models. In particular, a series of works
by Economou and Kanta [51, 53] provide detailed analysis for retrial models with constantretrial rate. Wang et al. [126, 127, 128] analyze also the models with classical retrial rate. To

17

be more precise, models in Wang et al. [127, 128] are devoted to strategic joining behavior of
secondary users in cognitive networks. It should be noted that these references are devoted
to M/M/1/1 type retrial queues only. Thus, the analyses of more general models might bepromising future topics.

3.9

General Retrial Times

Most of researches assume the exponential retrial time. Only a few references are devoted
to models with other retrial time distributions where each customer in the orbit acts independently of other customers [34, 117, 119]. In the current literature, there is not anexact analysis for this type of settings and only some approximations or simulations arepresented. Thus, researches in this direction may be highly appreciated.

3.10

Other Performance Measures

In almost the work mentioned above, the main quantity of interest is the stationary queue
length distribution. A few references are devoted to some new quantities of interest. In particular, channel idle period is analyzed by Artalejo and Gomez-Corral in [13]. Distributionsof the successful and blocked events are studied in [5, 6, 7]. Maximum queue length in busy
period is presented in [9] while that in a fixed time interval is analyzed by Gomez-Corraland Garcia [68]. Artalejo and Lopez-Herrero [17] analyze the distribution of the number of
retrials of a tagged customer in M/G/1/1 and M/M/c/c retrial queues. Falin [54] obtain
the distribution of the number of retrials for M/G/1/1 retrial queues. Dragieva [46, 47]
studies the distribution of the number of retrials in model with finite source with arbitrary
service time distribution. Gao et al. [63] obtain the distribution for number of retrials in
an M/M/1/1 retrial queue with constant retrial rate and impatient customers.

4

Concluding Remarks

In this paper, we have surveyed the main theoretical results for retrial queueing models.
We have also investigated retrial queueing models arising from real applications such as callcenters, random access protocols, cellular networks etc. We hope that this paper can beserved as a basic reference for researchers who want to enter and deepen this field. Because
the retrial queue literature is rich, we also refer to some earlier survey papers [130, 56, 11,18

66, 18], two books [57, 19] and the recent Special Issue [69]. Most of references in thispaper is for continuous time retrial queues. We refer to Nobel [94] for a survey on resultsof discrete time retrial queues. Sections 1 and 2 are partially based on the dissertation of
the author [100].

Acknowledgments
Tuan Phung-Duc was supported in part by JSPS KAKENHI Grant Number 26730011.
The author would like to thank two anonymous reviewers whose comments greatly helped
to improve the presentation of the paper. I would like to thank the Editors of the book,especially Professor Shoji Kasahara for giving me an opportunity to write this survey.
The author would like to devote this paper to the memory of Professor Jesus Artalejo
who was a leading researcher in the fields of Queueing Theory and Mathematical Biology
publishing more than one hundred papers and was a co-author and friend of the author ofthis paper.

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