A Spectral Method for a Nonpreemptive Priority BMAP/G/1 QUEUE

A batch Markov arrival process (BMAP) X* = (N, J) is a 2-dimensional Markov process with two components, one is the counting process N and the other one is the phase process J. It is proved that the phase process is a time-homogeneous Markov chain with a finite state-space, or for short, Markov chain. In this paper, a new and inverse problem is proposed firstly: given a Markov chain J, can we deploy a process N such that the 2-dimensional process X* = (N, J) is a BMAP? The process X* = (N, J) is said to be an adjoining BMAP for the Markov chain J. For a given Markov chain the adjoining processes exist and they are not unique. Two kinds of adjoining BMAPs have been constructed. One is the BMAPs with fixed constant batches, the other one is the BMAPs with independent and identically distributed (i.i.d) random batches. The method we used in this paper is not the usual matrix-analytic method of studying BMAP, it is a path-analytic method. We constructed directly sample paths of adjoining BMAPs. The expressions of characteristic (D k , k = 0, 1, 2 · · ·) and transition probabilities of the adjoining BMAP are obtained by the density matrix Q of the given Markov chain J. Moreover, we obtained two frontal Theorems. We present these expressions in the first time.

As various video services become popular, video streaming will dominate the mobile data traffic. The H.264 standard has been widely used for video compression. As the successor to H.264, H.265 can compress video streaming better, hence it is gradually gaining market share. However, in the short term H.264 will not be completely replaced, and will co-exist with H.265. Using H.264 and H.265 standards, three types of frames are generated, and among different types of frames exist dependencies. Since the radio resources are limited, using dependencies and quantities of frames in buffers, an appropriate time division transmission policy can be applied to transmit different types of frames sequentially, in order to avoid the occurrence of video carton or decoding failure. Polling models with batch Markovian arrival process (BMAP) and across-queue state-dependent service discipline are considered to be effective means in the design and optimization of appropriate time division transmission policies. However, the BMAP and across-queue state-dependent service discipline of the polling models lead to the large state space and several coupled state transition processes, which complicate the performance analysis. There have been very few researches in this regard. In this paper, a polling model of this type is analyzed. By constructing a supplementary embedded Markov chain and applying the matrix-analytic method based on the semi-regenerative process, the expressions of important performance measures including the joint queue length distribution, the customer blocking probability and the customer mean waiting time are obtained. The analysis will provide inspiration for analyzing the polling models with BMAP and across-queue state-dependent service discipline, to guide the design and optimization of time division transmission policies for transmitting the video compressed by H.264 and H.265.

Tandem queues are good mathematical models for description of information transmission in various communication systems and networks. These queues play also an important role for the validation of different decomposition algorithms designed for investigating more general queueing networks. So, their investigation is interesting for theory and applications. In this paper, we consider tandem queue suitable for modeling the systems and networks where information flows are correlated and bursty what is typical for many modern telecommunication networks. Possible correlation of customers inter-arrival times and batch arrivals are taken into account via of consideration of the Batch Markovian Arrival Process (BMAP) as input stream to the system. The system consists of two stations. The service time at the station 1 is assumed to be generally distributed. There is no buffer at this station, and customers who meet the busy server repeat attempts to enter the system in random time intervals. The service process at the station 2 is assumed to be described by the continuous time Markov chain with a finite state space. This assumption holds good, e.g., if the station 2 has a finite buffer, consists of a finite number of identical or heterogeneous servers where the service time distribution is assumed to be of PH (PHase) type. Markov chain embedded at service completion epochs at the station 1 and the process of system states at arbitrary time are under study. Ergodicity condition and algorithms for computing the steady state probabilities are presented.

This chapter presents the Enhanced-real time Polling System (E-rtPS) with RT- Batch Markovian Arrival Process and proactive resource allocation framework to resolve the real-time network traffic issues and solve the interference problem optimistically. It proposes proactive resource allocation framework offers less computational overhead and yet again well-nigh performance in terms of resource allocation to maximize the transmission rate of each user. The chapter examines real-time results shows that the proposed framework outperforms the existing Institute of Electrical and Electronics Engineers (IEEE) 802.16 services in terms of throughput, session setup delay, and packet reception ratio. In IEEE 802.16, there are different Medium Access Control scheduling services, such as Unsolicited Grant Service, rtPS, and non-real-time Polling Service to provide better quality of services. The chapter presents an adaptive framework for dynamic speed management in smart cities. Since smart phone usage has risen to half a billion phones, mobile devices and connections account for 89 percent of the usage of mobile data traffic.

The author combines a survey of Markov-based efforts in automated composition with a tutorial demonstrating how various theoretical properties associated with Markov processes can be put to practical use. The historical background is traced from A. A. Markov’s original formulation through to the present. A digression into Markov-chain theory introduces ‘waiting counts’ and ‘stationary probabilities’. The author’s Demonstration 4 for solo clarinet illustrates how these properties affect the behavior of a melody composed using Markov chains. This simple example becomes a point of departure for increasingly general interpretations of the Markov process. The interpretation of ‘states’ is reevaluated in the light of recent musical efforts that employ Markov chains of higher-level objects and in the light of other efforts that incorporate relative attributes into the possible interpretations. Other efforts expand Markov’s original definition to embrace ‘Nth-order’ transitions, evolving transition matrices and chains of chains. The remainder of this article contrasts Markov processes with alternative compositional strategies.

Queueing analysis and optimal control of [formula omitted] and [formula omitted] systems

To improve the service quality and save the system cost of the cloud computing center, this paper studies the joint optimization problem of energy consumption and performance of cloud computing centers with a batch Markovian arrival process. The system has multiple parallel processors and the processing time of each processor follows phase type distribution. The system has finite buffer. We construct the system as a BMAP/PH/N/M queueing system and analyze the performance of the system based on queuing theory. An optimization model is established by combing the energy consumption and system performance measures. The optimal number of servers is analyzed. Some managerial insights are given by numerical analysis.

A retrial queueing system with the batch Markovian arrival process and semi-Markovian service is investigated. We suppose that the intensity of retrials linearly depends on the number of repeated calls. The distribution of the number of calls in the system is the subject of research. Asymptotically quasi-Toeplitz 2-dimensional Markov chains are introduced into consideration and applied for solving the problem.

In this paper, we study a multi-server queueing system with retrials and an infinite orbit. The arrival of primary customers is described by a batch Markovian arrival process (BMAP), and the service times have a phase-type (PH) distribution. Previously, in the literature, such a system was mainly considered under the strict assumption that the intervals between the repeated attempts from the orbit have an exponential distribution. Only a few publications dealt with retrial queueing systems with non-exponential inter-retrial times. These publications assumed either the rate of retrials is constant regardless of the number of customers in the orbit or this rate is constant when the number of orbital customers exceeds a certain threshold. Such assumptions essentially simplify the mathematical analysis of the system, but do not reflect the nature of the majority of real-life retrial processes. The main feature of the model under study is that we considered the classical retrial strategy under which the retrial rate is proportional to the number of orbital customers. However, in this case, the assumption of the non-exponential distribution of inter-retrial times leads to insurmountable computational difficulties. To overcome these difficulties, we supposed that inter-retrial times have a phase-type distribution if the number of customers in the orbit is less than or equal to some non-negative integer (threshold) and have an exponential distribution in the contrary case. By appropriately choosing the threshold, one can obtain a sufficiently accurate approximation of the system with a PH distribution of the inter-retrial times. Thus, the model under study takes into account the realistic nature of the retrial process and, at the same time, does not resort to restrictions such as a constant retrial rate or to rough truncation methods often applied to the analysis of retrial queueing systems with an infinite orbit. We describe the behavior of the system by a multi-dimensional Markov chain, derive the stability condition, and calculate the steady-state distribution and the main performance indicators of the system. We made sure numerically that there was a reasonable value of the threshold under which our model can be served as a good approximation of the BMAP/PH/N queueing system with the PH distribution of inter-retrial times. We also numerically compared the system under consideration with the corresponding queueing system having exponentially distributed inter-retrial times and saw that the latter is a poor approximation of the system with the PH distribution of inter-retrial times. We present a number of illustrative numerical examples to analyze the behavior of the system performance indicators depending on the system parameters, the variance of inter-retrial times, and the correlation in the input flow.

A tandem retrial queueing system with two Markovian flows and reservation of channels

This paper focuses on simple exponential approximations for tail probabilities of the steady-state waiting time in infinite-capacity multiserver queues based on small-tail asymptotics. For the GI/GI/s model, we develop a heavy-traffic asymptotic expansion in powers of one minus the traffic intensity for the waiting-time asymptotic decay rate. We propose a two-term approximation for the asymptotic decay rate based on the first three moments of the interarrival-time and service-time distributions. We also suggest approximating the asymptotic constant by the product of the mean and the asymptotic decay rate. We evaluate the exponential approximations based on the exact asymptotic parameters and their approximations by making comparisons with exact results obtained numerically for the BMAP/GI/1 queue, which has a batch Markovian arrival process, and the GI/GI/s queue. Numerical examples show that the exponential approximations are remarkably accurate, especially for higher percentiles, such as the 90th percentile and beyond.

We consider a finite-buffer single server queueing system with queue-length dependent vacations where arrivals occur according to a batch Markovian arrival process (BMAP). The service discipline is P-limited service, also called E-limited with limit variation (ELV) where the server serves until either the system is emptied or a randomly chosen limit of L customers has been served. Depending on the number of customers present in the system, the server will monitor his vacation times. Queue-length distributions at various epochs such as before, arrival, arbitrary and after, departure have been obtained. Several other service disciplines like Bernoulli scheduling, nonexhaustive service, and E-limited service can be treated as special cases of the P-limited service. Finally, the total expected cost function per unit time is considered to determine locally optimal values N* of N or a maximum limit L^* of L^ as the number of customers served during a service period at a minimum cost.

In this paper, we consider a single server queuing model with an infinite buffer in which customers arrive according to a batch Markovian arrival process (BMAP). The services are offered in two modes. In mode 1, the customers are served one at a time and in mode 2 customers are served in groups of varying sizes. Various costs for holding, service and switching are imposed. For a given hysteretic strategy, we derive an expression for the cost function from which an optimal hysteretic control can be obtained. Illustrative numerical examples are presented.

Algorithm for computing the queue length distribution at various time epochs in DMAP/G(1, a, b)/1/N queue with batch-size-dependent service time

To measure the degree of basic service of wireless sensor network (WSN) with denial of service (DoS) attacks, it is necessary to evaluate survivability of WSN. This paper presents new method based on services and Markov Chain. It distinguishes services, analyzes influence of services under DoS attacks, gets the formula of survivability. Probability of states is obtained by markov chain, then the quantitve survivability is getted in different states. Simulations prove that DoS attacks obviously decrease the survivability of WSN, but adding node energy and improving node density in certain range can reduce the influence of DoS attacks.