带有负顾客的M/M/c多重工作休假排队

考虑服务台在休假期间不是完全停止工作,而是以相对于正常服务期低些的服务率服务顾客的M/M/c工作休假排队模型.在此模型基础上,针对现实的M/M/c排队模型中可能出现的外来干扰因素,提出了带有负顾客的M/M/c工作休假排队这一新的模型.服务规则为先到先服务.工作休假策略为空竭服务异步多重工作休假.抵消原则为负顾客一对一抵消处于正常服务期的正顾客,若系统中无处于正常服务期的正顾客时,到达的负顾客自动消失,负顾客不接受服务.首先,由该多重休假模型得到其拟生灭过程及生成元矩阵,然后运用矩阵几何方法给出系统队长的稳态分布表达式和若干系统指标.     

推广的多重休假$M^X/G/1$排队系统

在平稳状态下,Baba利用补充变量方法研究了多重休假的MX/G/1排队,但作者假定了休假时间和服务时间都有概率密度函数.本文考虑推广的多重休假MX/G/1排队,在假定休假时间和服务时间都是一般概率分布函数下,我们研究了队长的瞬态和稳态性质.通过引进"服务员忙期"和使用不同于Baba文中使用的分析技术,我们导出了在任意时刻t瞬态队长分布的L变换的递推表达式和稳态队长分布的递推表达式,以及平稳队长的随机分解.特别地,通过本文可直接获得多重休假的M/G/1与标准的MX/G/1排队系统相应的结果.     

带RCE抵消策略的负顾客M/M/1工作休假排队系统

考虑服务员在休假期间不是完全停止工作,而是以相对于正常工作时低些的速率服务顾客的M/M/1工作休假排队模型.在此模型基础上,笔者针对现实的M/M/1排队模型中可能出现的外来干扰因素,提出了带RCE(Removal of Customers at the End)抵消策略的负顾客M/M/1工作休假排队这一新的模型.服务规则为先到先服务.工作休假策略为空竭服务多重工作休假.抵消原则为负顾客一对一抵消队尾的正顾客,若系统中无正顾客时,到达的负顾客自动消失,负顾客不接受服务.使用拟生灭过程和矩阵几何解方法给出了系统队长的稳态分布,证明了系统队长和等待时间的随机分解结果并给出稳态下系统中正顾客的平均队长和顾客在系统中的平均等待时间.     

有Bernoulli休假和可选服务的Geo/G/1重试排队

讨论了有Bernoulli休假策略和可选服务的离散时间Geo/G/1重试排队系统.假定一旦顾客发现服务台忙或在休假就进入重试区域,重试时间服从几何分布.顾客在进行第一阶段服务结束后可以离开系统或进一步要求可选服务.服务台在每次服务完毕后,可以进行休假,或者等待服务下一个顾客.还研究了在此模型下的马尔可夫链,并计算了在稳态条件下的系统的各种性能指标以及给出一些特例和系统的随机分解.     

带负顾客的Geom/Geom/1单重休假排队(英文)

本文研究了正负顾客到达均服从几何分布,服务台在工作休假期以较低的服务速率运行的 Geom/Geom/1休假排队.运用嵌入马尔科夫链和矩阵分析法,得到了系统中等待队长和稳态队长的概率母函数,并从证明过程和结果中,分别得到了服务台在闲期、忙期、工作休假期、正规忙期的概率.     

带有Bernoulli控制策略的M/M/1多重休假排队模型

研究具有Bernoulli控制策略的M/M/1多重休假排队模型: 当系统为空时, 服务台依一定的概率或进入闲期, 或进入普通休假状态, 或进入工作休假状态. 对该模型, 应用拟生灭(QBD)过程和矩阵几何解的方法, 得到了过程平稳队长的具体形式, 在此基础上, 还得到了平稳队长和平稳逗留时间的随机分解结果以及附加队长分布和附加延迟的LST的具体形式. 结果表明, 经典的M/M/1排队, M/M/1多重休假排队, M/M/1多重工作休假排队都是该模型的特殊情形.     

带有部分工作休假和休假中断的M/M/c排队

考虑了一个带有部分工作休假和休假中断的多服务台M/M/c排队.在休假期,d(d相似文献   

M/G/1非空竭服务休假排队系统的平衡条件分析

讨论了一般非空竭服务M/G/1型休假排队系统的嵌入更新过程常返的条件,为稳态队长与等待时间的随机分解奠定理论基础.并且在独立休假策略下进一步简化Fuhrman与Cooper(1985)休假排队系统的随机分解的条件,并得到完整的随机分解结构.     

具有双阶段休假模式的M/M/1排队系统均衡策略研究

双阶段休假模式是一种将单重工作休假策略与多重休假策略相结合而得到一种更复杂且符合实际生活的休假模式,在该系统中工作台间断的进行工作休假与休假.基于排队博弈理论对具有双阶段休假模式的M/M/1排队系统,顾客在完全可见和几乎可见两种信息程度下的均衡进队策略进行研究,推导得出不同信息程度下顾客进队均衡策略.最后,对银行排队系统进行实例分析,并对两种信息程度下参数灵敏性进行分析.     

N策略带启动时间的Geom/Geom/1工作休假排队

考虑N策略带启动时间的Geom/Geom/1工作休假排队,服务员在休假期间并未完全停止工作而是以较低的速率为顾客服务.运用拟生灭链和矩阵几何解方法,给出了该模型的稳态队长的分布和等待时间的概率母函数,并证明了队长和等待时间的条件随机分解结构.     

Analysis of the queue-length distribution for the discrete-time batch-service Geo/G/1/K queue

In this paper, we consider a discrete-time finite-capacity queue with Bernoulli arrivals and batch services. In this queue, the single server has a variable service capacity and serves the customers only when the number of customers in system is at least a certain threshold value. For this queue, we first obtain the queue-length distribution just after a service completion, using the embedded Markov chain technique. Then we establish a relationship between the queue-length distribution just after a service completion and that at a random epoch, using elementary ‘rate-in = rate-out’ arguments. Based on this relationship, we obtain the queue-length distribution at a random (as well as at an arrival) epoch, from which important performance measures of practical interest, such as the mean queue length, the mean waiting time, and the loss probability, are also obtained. Sample numerical examples are presented at the end.     

Algorithmic analysis of theMAP/PH/1 retrial queue

In this paper the distribution of the maximum number of customers in a retrial orbit for a single server queue with Markovian arrival process and phase type services is studied. Efficient algorithm for computing the probability distribution and some interesting numerical examples are presented.     

M/G/1/MLPS compared with M/G/1/PS within service time distribution class IMRL

Multilevel processor-sharing (MLPS) disciplines were originally introduced by Kleinrock (in computer applications 1976) but they were forgotten for years. However, due to an application related to the service differentiation between short and long TCP flows in the Internet, they have recently gained new interest. In this paper we show that, if the service time distribution belongs to class IMRL, the mean delay in the M/G/1 queue is reduced when replacing the PS discipline with any MLPS discipline for which the internal disciplines belong to {FB, PS}. This is a generalization of our earlier result where we restricted ourselves to the service time distribution class DHR, which is a subset of class IMRL.     

Equilibrium results for the M/G/k group-arrival loss system

In this paper we consider a specific M/G/k group-arrival loss system, under statistical equilibrium and two cases of acceptance policy. In the first case the system works under the partial acceptance policy. Explicit results are obtained for the corresponding stationary distribution, which extend previous relevant results. In the second case, where the system works under the all-or-nothing acceptance policy, a sufficient condition is given for the stationary distribution to have a closed-product form. In both cases customers depart individually, while the joint service time distribution of the accepted members of a group may depend both on its initial and the accepted size plus an additional condition.     

Sojourn time asymptotics in the M/G/1 processor sharing queue

We show for the M/G/1 processor sharing queue that the service time distribution is regularly varying of index -ν, ν non-integer, iff the sojourn time distribution is regularly varying of index -ν. This result is derived from a new expression for the Laplace–Stieltjes transform of the sojourn time distribution. That expression also leads to other new properties for the sojourn time distribution. We show how the moments of the sojourn time can be calculated recursively and prove that the kth moment of the sojourn time is finite iff the kth moment of the service time is finite. In addition, we give a short proof of a heavy traffic theorem for the sojourn time distribution, prove a heavy traffic theorem for the moments of the sojourn time, and study the properties of the heavy traffic limiting sojourn time distribution when the service time distribution is regularly varying. Explicit formulas and multiterm expansions are provided for the case that the service time has a Pareto distribution. This revised version was published online in June 2006 with corrections to the Cover Date.     

带单重指数工作休假和休假中断的GI／M／1的排队系统

本文主要研究带有单重指数工作休假和休假中断策略的GI／M／1排队模型。利用分块矩阵表示出嵌入马尔可夫链的转移矩阵,并运用矩阵几何解的方法求得到达时刻队长的稳态分布,而且证明了其可以分解为三个独立随机变量的分布的和。     

Continuity theorems for the M/M/1/n queueing system

In this paper continuity theorems are established for the number of losses during a busy period of the M/M/1/n queue. We consider an M/GI/1/n queueing system where the service time probability distribution, slightly different in a certain sense from the exponential distribution, is approximated by that exponential distribution. Continuity theorems are obtained in the form of one or two-sided stochastic inequalities. The paper shows how the bounds of these inequalities are changed if further assumptions, associated with specific properties of the service time distribution (precisely described in the paper), are made. Specifically, some parametric families of service time distributions are discussed, and the paper establishes uniform estimates (given for all possible values of the parameter) and local estimates (where the parameter is fixed and takes only the given value). The analysis of the paper is based on the level crossing approach and some characterization properties of the exponential distribution. Dedicated to Vladimir Mikhailovich Zolotarev, Victor Makarovich Kruglov, and to the memory of Vladimir Vyacheslavovich Kalashnikov.     

The GI/M/1 queue with exponential vacations

In this paper, we give a detailed analysis of the GI/M/1 queue with exhaustive service and multiple exponential vacation. We express the transition matrix of the imbedded Markov chain as a block-Jacobi form and give a matrix-geometric solution. The probability distribution of the queue length at arrival epochs is derived and is shown to decompose into the distribution of the sum of two independent random variables. In addition, we discuss the limiting behavior of the continuous time queue length processes and obtain the probability distributions for the waiting time and the busy period.     

The Periodic BMAP/PH/c Queue

In queueing theory, most models are based on time-homogeneous arrival processes and service time distributions. However, in communication networks arrival rates and/or the service capacity usually vary periodically in time. In order to reflect this property accurately, one needs to examine periodic rather than homogeneous queues. In the present paper, the periodic BMAP/PH/c queue is analyzed. This queue has a periodic BMAP arrival process, which is defined in this paper, and phase-type service time distributions. As a Markovian queue, it can be analysed like an (inhomogeneous) Markov jump process. The transient distribution is derived by solving the Kolmogorov forward equations. Furthermore, a stability condition in terms of arrival and service rates is proven and for the case of stability, the asymptotic distribution is given explicitly. This turns out to be a periodic family of probability distributions. It is sketched how to analyze the periodic BMAP/M _t /c queue with periodically varying service rates by the same method.     

On the Remaining Service Time upon Reaching a Given Level in M/G/1 Queues

The ability of effectively finding the distribution of the remaining service time upon reaching a target level in M/G/1 queueing systems is of great practical importance. Among other things, it is necessary for the estimation of the Quality-of-Service (QoS) provided by Asynchronous Transfer Mode (ATM) networks. The previous papers on this subject did not give a comprehensive solution to the problem. In this paper an explicit formula for this distribution is given. This formula is general as it includes any initial level of the length of the queue, any type of service distribution (heavy tails) and any traffic intensity ρ. Moreover, it is easy to use and fast in computation. To show this several numerical examples are presented. In addition, a solution of the similar problem in G/M/1 queues (which is the distribution of the remaining interarrival time) is given.