具有不耐烦用户VOD单播系统的排队分析研究

本文运用排队论的方法对视频服务系统单播情形下的部分指标进行了定性分析,分析了系统服务状态,以用户等待平均数和系统吞吐量为主要考察对象,给出了用户等待平均数与系统服务强度及用户耐心程度的关系,阐述了系统吞吐量受服务强度及用户耐心程度的影响.仿真结果表明,这也是一种节省资源、提高视频服务水平的有效途径.     

VOD系统批处理调度策略优化研究

大规模视频点播（VOD）业务需要消耗大量的网络带宽和服务器资源，基于多点组播的批处理技术允许服务器的不增加额外系统资源的情况下发送相同数据到多个客户端，从而达到系统资源利用率的最大化，通常使用的批处理调度策略有FCFS、MQL和MFQL三种，但是这些方案没有得到令人满意的结果，本文提出一种新策略，称之为MDP，即最大撤销概念，其基本思想是在组空间搜索用户请救撤概率和最大值，MDP以等待服务的所有组中撤销概率的和最大为调度决策依据。保障到达系统的用户因不能忍受等待时间太长而退出的概念最小，通过分析与仿真实验，MDP方案在系统蚕吐量，平均等待时间，用户请求撤销概率和不公平性等系统性能参量方面获得优良结果。     

一种光盘库的短节目调度算法

海量的视频资料可以直接存储在光盘库(或磁带库)等大容量存储设备中，为了提高系统的性能，必须研究有效的调度策略，提出了一种基于光盘库的短节目视频点播系统调度算法——CMF（combined MQL／FCFS)算法。该算法依据等待请求队列的权重来选择所需服务的盘片，并根据节目请求到达率的不同来调整等待队列中请求的等待时间权重，使系统获得效率和公平之间的平衡，仿真结果表明，与MQL和FCFS算法相比，该算法有效减少了用户请求的撤消率，提高了系统的吞吐量。     

视频网格动态负载加权平滑均衡算法

为实现网格中视频资源服务的动态负载均衡,对一种动态负载加权均衡算法进行改进。利用监测与发现系统收集每台视频服务器的CPU利用率等主要负载参数,运用上述参数加权得到综合负载,对相邻时刻的负载做平滑处理以避免调度抖动。通过比较平滑后的动态负载值与服务器综合负载阈值进行动态调度,改变相应节点的负载,避免视频服务器间的负载失衡。实验结果表明,该算法能有效降低系统平均服务延迟时间并提高吞吐量,从而提升视频资源网格服务的整体性能。     

基于数据挖掘的秦岭北麓西安段古村镇旅游智能推荐系统设计

为了促进智慧旅游建设，保证旅游系统能够为用户提供个性化服务，以秦岭北麓西安段古村镇旅游为例，基于数据挖掘技术设计一种旅游智能推荐系统。从规模、优越度与聚集度三方面挖掘该地旅游资源空间分布特征；将用户对视频、图片、文本三种类型旅游信息浏览时间的加权平均值作为兴趣度函数，确定每个古村镇的用户兴趣度；将感兴趣的景点保存到相同集合中，通过规则匹配，实现智能推荐；分别设计图形用户界面、智能分析与智能推荐服务模块，完成系统整体设计。仿真实验表明，该系统可实现旅游个性化智能推荐，提高用户满意度。     

用户需求感知的D2D视频分发机制

针对目前的D2D多播内容分发机制因冗余传输过高而导致簇头节点能量效率以及系统频谱效率低的问题,提出了一种基于用户需求感知的D2D视频分发机制。通过对用户视频服务请求进行预测,在为服务请求用户分发视频的同时将视频推送给潜在服务用户。首先,根据用户的兴趣、视频的流行度以及用户设备的剩余能量等估算出潜在服务用户接受视频推送的意愿;其次,提出视频分发服务效用值度量来衡量视频分发服务的价值,并对最优化簇头能量效率问题进行建模;最后,提出最优化簇头能量效率的D2D视频分发机制。实验结果表明所提出的D2D视频分发机制相较于传统分发机制其簇头的能量效率及系统吞吐量有着明显的优势。     

交互式VOD视频源非线性智能分段算法

为了实现视频点播系统的交互性功能，经典的方法是将视频源均匀分割存储，以实现其有限的交互性．从VOD系统的实际出发，该文提出了非线性智能分段方法NLSM它的核心思想是在一段时间内对一定步长范围内VCR交互性操作的到达情况进行统计求和，计算系统总体时频总面积，并按信道数目将各时间段与VCR发生频率的乘积把总面积均匀等分，从而实现视频源在时间轴上的非线性分割．针对均匀的周期性广播分段方法ESM方案进行比较仿真研究，获得了用户VCR请求的响应等待时间的理想结果和最佳的用户等待时间标准偏差、系统拒绝概率和信道吞吐量等方面的优良性能．NLSM算法对历史信息进行数据挖掘，揭示用户对视频节目内容感兴趣程度的潜在分布规律，具有良好的自适应能力和学习智能．算法性能分析和仿真实验的结果说明该方法在实际应用中是可行且高效的．     

一种新的基于补丁优先的流调度算法

通过对现有流调度算法的深入研究,提出了一种新的流媒体调度算法——补丁优先的最大等待队列算法。该算法依据请求等待队列的权重来选择服务队列,使系统获得效率和公平性之间的平衡。理论分析和仿真实验表明:与FCFS、MQL和MPQL算法相比,该算法有效减少了用户请求的撤消率、平均等待时间、不公平性,提高了系统的吞吐量,是一种有效的调度策略。     

基于忍耐度的流媒体批调度算法

批处理是一种简单有效的流媒体调度机制。对于同一网络延迟,有些用户可以接受,有些用户则无法接受,调度时应区别对待。本文提出了一种基于忍耐度的批调度算法,考虑用户忍耐度不同,合理延迟用户请求,等待更多的用户请求到达,以便在下一次批处理时启动一个组播流来服务更多的用户,但不至于延迟过度,发生用户食言,从而提高资源有效利用率。仿真结果表明该算法能有效降低用户食言率。     

基于物联网传感的体育训练强度实时监控系统

为了提高体育训练强度,实时监控系统的负载和吞吐量性能，提出基于物联网传感的体育训练强度实时监控系统设计。运用物联网传感技术，设计系统硬件框图；依据系统运行需求，采用红外传感器、三轴加速度传感器、心率带、脉搏传感器、体温监测探头组成物联网传感网，采集体育训练者心率、呼吸次数等身体机能参数。建立体育训练强度实时监控程序，生成身体机能与体育训练强度对应模型，实现了体育训练强度实时监控。系统测试结果：系统功能测试结果与预期结果一致，系统负载和吞吐量测试指标值均在系统设计标准范围内，达到系统设计的预期目标。     

面向在线客服系统的调度算法研究

Different from traditional customer service systems, online customer service systems offer business services for multiple customers simultaneously, which makes the adaptation and scheduling between service providers and customers a big challenge. Based on the characteristics of online customer service, this paper proposes a scheduling model for online customer service systems. The scheduling model is composed of three constituents: a multi-priority customer queue, the states of the scheduling system and the transition relations between them, and the correspondence between scheduling strategies and states of the system. Its scheduling algorithm is designed. Experiments verify the rationality of the scheduling model and the effectiveness of the scheduling algorithm. In comparison to the operating customer service system, the algorithm can not only considerably reduce the average waiting time of customers, but also achieve load balancing among service providers, when guaranteeing high quality of services.     

Exploring wait tolerance in effective batching for video-on-demand scheduling

In a video-on-demand (VOD) environment, batching requests for the same video to share a common video stream can lead to significant improvement in throughput. Using the wait tolerance characteristic that is commonly observed in viewers behavior, we introduce a new paradigm for scheduling in VOD systems. We propose and analyze two classes of scheduling schemes: the Max_Batch and Min_Idle schemes that provide two alternative ways for using a given stream capacity for effective batching. In making a video selection, the proposed schemes take into consideration the next stream completion time, as well as the viewer wait tolerance. We compared the proposed schemes with the two previously studied schemes: (1) first-come-first-served (FCFS) that schedules the video with the longest waiting request and (2) the maximum queue length (MQL) scheme that selects the video with the maximum number of waiting requests. We show through simulations that the proposed schemes substantially outperform FCFS and MQL in reducing the viewer turn-away probability, while maintaining a small average response time. In terms of system resources, we show that, by exploiting the viewers wait tolerance, the proposed schemes can significantly reduce the server capacity required for achieving a given level of throughput and turn-away probability as compared to the FCFS and MQL. Furthermore, our study shows that an aggressive use of the viewer wait tolerance for batching may not yield the best strategy, and that other factors, such as the resulting response time, fairness, and loss of viewers, should be taken into account.     

Unreliable multi-server system with controllable broadcasting service

Consideration was given to the multi-server queuing system with an infinite buffer. The customer arrivals obey a Markov arrival flow, the time of customer service having a phasetype distribution. service may be done with errors. If at the instant of customer arrival the number of busy servers is less than some threshold, then the customer is copied to all free servers which service it. If at that instant the number of busy servers is not less than some threshold, then the customer is serviced by a single server. The stationary distribution of the number of customers and their sojourn time were determined. The impact of the threshold value on the probability of successful service of the customer was studied numerically.     

Markov queueing system with finite buffer and negative customers affecting the queue end

A multiserver queueing system with finite buffer, Markov input flow, and Markov (general) service process of all customers on servers with the number of process states and intensities of inter-phase transitions depending on the number of customers in the system is considered. A Markov flow of negative customers arrives to the system; one negative customer “kills” one positive customer at the end of the queue. A recurrent algorithm for computing stationary probabilities of system states is obtained; and a method for calculating stationary distribution of waiting time before starting service of a positive customer is proposed.     

Optimal control of parallel queues with impatient customers

We consider a queueing system with a number of identical exponential servers. Each server has its own queue with unlimited capacity. The service discipline in each queue is first-come-first-served (FCFS). Customers arrive according to a state-dependent Poisson process with an arrival rate which is a non-increasing function of the number of customers in the system. Upon arrival, a customer must join a server’s queue according to a stationary state-dependent policy, where the state is taken to be the number of customers in servers’ queues. No jockeying among queues is allowed. Each arriving customer is limited to a generally distributed patience time after which it must depart the system and is considered lost. Two models of customer behavior are considered: deadlines until the beginning of service and deadlines until the end of service. We seek an optimal policy to assign an arriving customer to a server’s queue. We show that, when the distribution of customer impatience satisfies certain property, the policy of joining shortest queue (SQ) stochastically minimizes the number of lost customers during any finite interval in the long run. This property is shown to always hold for the case of deterministic customer impatience.     

Single server retrial queue with group admission of customers

We consider a retrial queueing system with a single server and novel customer׳s admission discipline. The input flow is described by a Markov Arrival Process. If an arriving customer meets the server providing the service, it goes to the orbit and repeats attempts to get service in random time intervals whose duration has exponential distribution with parameter dependent on the customers number in orbit. Server operates as follows. After a service completion epoch, customers admission interval starts. Duration of this interval has phase type distribution. During this interval, primary customers and customers from the orbit are accepted to the pool of customers which will get service after the admission interval. Capacity of this pool is limited and after the moment when the pool becomes full before completion of admission interval all arriving customers move to the orbit. After completion of an admission interval, all customers in the pool are served simultaneously by the server during the time having phase type distribution depending on the customers number in the pool. Using results known for Asymptotically Quasi-Toeplitz Markov Chains, we derive stability condition of the system, compute the stationary distribution of the system states, derive formulas for the main performance measures and numerically show advantages of the considered customer׳s admission discipline (higher throughput, smaller average number of customers in the system, higher probability to get a service without visiting the orbit) in case of proper choice of the capacity of the pool and the admission period duration.     

A new procedure to estimate waiting time in GI/G/2 system by server observation

Most GI/G/2 queueing formulae need the variance of inter-arrival time, which is in many cases more difficult to estimate than the other values used in the formulae such as the mean of inter-arrival time, mean of service time and variance of service time. This paper presents a new GI/G/2 queueing formula which uses a slightly different set of data easier to obtain than the variance of inter-arrival time. The key variables are the numbers of system busy periods and system idle periods. Also, it is shown, by simulation, that the waiting time estimation error from the new formula is far less than other popular queueing formulae which use the first two moments of service time and inter-arrival time over a wide range of coefficient of variation.Scope and purposeWaiting is very common in our daily life, and the estimation is sometimes very important for the design of service and manufacturing systems. If the number of barbers at a barber's shop is too small, customers frequently wait for the service too long. If the number of machines in a manufacturing shop is too small, the production lead time from order entry to product delivery can be very long.The waiting time is closely related not only to the average service requirement but also to the variability of it. If customers require service at the same time, the average waiting time of the customers will be longer than the average waiting time with even requests. Traditionally, the variance of inter-arrival time has been used to represent the variability; however, estimation of the variance needs observation of customer arrivals, which often needs much effort. This paper presents another procedure to estimate the waiting time. This procedure does not need the observation of customers. The estimation of waiting time for bank teller machines can be a good application example of this new procedure because the machines do not have the arrival data of the customers. The procedure presented here is for a two parallel server case.     

A perishable inventory system with service facilities and retrial customers

In this article, we present a continuous review perishable (s, S) inventory system with a service facility consisting of finite waiting room and a single server. The customers arrive according to a Markovian arrival process (MAP). The individual customer’s unit demand is satisfied after a random time of service which is assumed to have phase-type distribution. The life time of each item and the lead time of reorders are assumed to have independent exponential distributions. Any arriving customer, who finds the waiting room is full, enters into the orbit of infinite space. These orbiting customers compete for service by sending out signals the duration between two successive attempts are exponentially distributed. The joint probability distribution of the number of customers in the waiting room, number of customers in the orbit and the inventory level is obtained in the steady-state case. Various stationary system performance measures are computed and total expected cost rate is calculated.     

Event-based optimization of admission control in open queueing networks

In this paper, we use the event-based optimization framework to study the admission control problem in an open Jackson network. The external arriving customers are controlled by an admission controller. The controller makes decision only at the epoch when an event of customer arrival happens. Every customer in the network obtains a fixed reward for each service completion and pays a cost with a fixed rate during its sojourn time (including waiting time and service time). The complete information of the system state is not available for the controller. The controller can observe only the number of total customers in the network. Based on the property of closed form solution of Jackson networks, we prove that the system performance is monotonic with respect to the admission probabilities and the optimal control policy has a threshold form. That is, when the number of total customers is smaller than a threshold, all of the arriving customers are admitted; otherwise, all are rejected. The sufficient condition and necessary condition of optimal policy are also derived. Furthermore, we develop an iterative algorithm to find the optimal policy. The algorithm can be executed based on a single sample path, which makes the algorithm online implementable. Simulation experiments are conducted to demonstrate the effectiveness of our approach.