VOD服务器性能及服务方式研究

在视频点播领域的关键性挑战就是以合理经济的成本为成千上万的潜在用户提供交互点播服务。为寻求这个问题的解决方案，对VOD服务器性能及服务方式选择进行了深入的研究。介绍了视频点播系统的几种分类方式，对影响视频点播服务器性能的关键要素和视频点播系统提供点播服务的方式进行了较全面和深入的分析，在此基础上提出和讨论了一些提高服务器性能和服务质量的实用策略和技术。     

基于协作缓存的VOD服务器端Cache设计

近年来,随着计算机网络技术和多媒体技术的发展,视频点播服务已逐渐成为现实。分布式VOD(VideoOnDemand)服务器系统的提出是为了支持更多的大量并发数据流,和单一服务器相比,这样的结构拥有更好的使用效率、可靠性和可扩展性。协作缓存CC(cooperativecache)技术将各服务器的内存协调工作,形成全局的cache。这样的结构不仅充分发挥了分布式VOD服务器结构的特点,同时也增大cache容量,提高系统全局命中率,从而提高了系统效率。该文在协作缓存技术基础上,针对流媒体和VOD系统的特点,提出了GBBcache替换算法。该算法以数据块的生命周期作为出发点,充分考虑了现有用户和请求接入用户的服务需求,提高了内存使用效率。笔者对该算法进行了理论分析,并证明了它在性能上与传统的cache替换算法相比的优越性。     

基于文件访问概率预测的VOD代理数据分布策略

针对校园网VOD系统应用中面临的网络带宽和服务器I/O带宽的瓶颈问题,通过对流媒体文件访问次数的统计回归分析,并结合Zipf分布规律和校园网流媒体点播的特征给出了一种流媒体文件访问概率的预测算法.根据该预测算法的结果提出了一种VOD代理服务器的数据分布策略.实验表明,该策略可以有效提高系统的服务性能和减少网络的阻塞.     

VOD系统中基于周期补丁算法的流调度策略

提出一介视频点播(VOD)系统流调度算法。在周期补丁流调度算法的基础上,利用分组和批处理理论,有效减少补丁流的数量。并采用门限算法,在一定程度上减少了因分组带来的增大平均用户等待时间的缺陷。根据节目的流行度,动态调整组播周期的大小。通过仿真实验,结果表明,利用该算法进行视频流调度效果良好,提高了VOD的服务性能。     

云环境下蚁群优化算法的视频点播视频流任务调度策略

针对云环境下大规模并发视频流调度过程中资源利用率低和负载不均的问题,提出一种基于蚁群优化(ACO)算法的视频点播(VOD)集群视频流任务调度策略VodAco。在分析视频流期望性能与服务器空闲性能的相关性、定义综合性能匹配度的基础上,建立数学模型,并采用蚁群优化思路进行最佳调度方案搜索。通过云仿真软件CloudSim实验表明,与轮询(RR)、贪婪(Greedy)算法相比,所提算法在任务完成时间、平台资源占有率、各节点性能负载均衡指标上具有较为明显的优势。     

视频点播系统的算法研究与实现

视频点播简称VOD（Video on Demand）,也称为交互式电视点播系统,意即根据用户的需要播放相应的视频节目,从根本上改变了用户过去被动式看电视的不足。改善系统需要改善各个领域的服务器性能,使用更先进,更高速的网络是一种方式,但成本高,高投入,这种途径往往不被接受。于是,一种以改进VOD算法与策略,以提高系统效率的方法被提出。本论文研究,以了解视频点播系统状态和对主要做了以下工作的基础上的一些关键技术的概念。     

一种基于调度的VOD系统的研究与实现

传统的视频点播(Video-On-Demand，VOD)系统中，服务器为每个用户请求分配一个独立的信道。这样过多的用户经常会造成系统的服务器I/O带宽或网络带宽的瓶颈。对服务器信道调度方案的设计是缓解这一瓶颈问题的一种有效方法。介绍了一种采用了可控多播(Controlled Multieast，CM)信道调度方案的系统的实现。测试结果表明，该系统可以节约带宽，增大视频点播服务的用户数目，较好地缓解传统VOD系统中的服务器I/O或网络带宽瓶颈问题。     

基于P2P网络的视频点播系统设计

视频点播(VOD)是以用户需求为主导的视频系统,如何提高视频点播(VCD)系统的可扩展性和在动态环境中的可靠性,成为视频点播系统大规模应用的关键.文中提出了一种新的基于P2P(Peer to peer)的VCD(video-on-demand)系统,并阐述了系统设计所采用的相关技术与方法.系统中考虑了节点均衡负载对系统整体性能的影响,采用服务器集中调度与节点分布协调管理相结合的资源定位方式,灵活的候选父节点策略使节点失效后能进行快速的失效恢复,提高了VOD系统的可扩展性和动态环境中的可靠性.     

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。     

视频点播系统中可控CIWP调度方案的研究与实现

节目调度是视频点播系统设计与实现中的一个重要问题。优化的节日调度可以极大地提高视频点播服务的性能。本文首先给出一种改进的CIWP调度算法——可控调度CIWP算法,然后给出其具体实现。理论与实践表明,该算法可以有效地节省服务器的资源,提高视频点播系统的服务水平。     

Nonlinear–nonquadratic optimal and inverse optimal control for stochastic dynamical systems

In this paper, we develop a unified framework to address the problem of optimal nonlinear analysis and feedback control for nonlinear stochastic dynamical systems. Specifically, we provide a simplified and tutorial framework for stochastic optimal control and focus on connections between stochastic Lyapunov theory and stochastic Hamilton–Jacobi–Bellman theory. In particular, we show that asymptotic stability in probability of the closed‐loop nonlinear system is guaranteed by means of a Lyapunov function that can clearly be seen to be the solution to the steady‐state form of the stochastic Hamilton–Jacobi–Bellman equation and, hence, guaranteeing both stochastic stability and optimality. In addition, we develop optimal feedback controllers for affine nonlinear systems using an inverse optimality framework tailored to the stochastic stabilization problem. These results are then used to provide extensions of the nonlinear feedback controllers obtained in the literature that minimize general polynomial and multilinear performance criteria. Copyright © 2017 John Wiley & Sons, Ltd.     

Partial‐state stabilization and optimal feedback control

In this paper, we develop a unified framework to address the problem of optimal nonlinear analysis and feedback control for partial stability and partial‐state stabilization. Partial asymptotic stability of the closed‐loop nonlinear system is guaranteed by means of a Lyapunov function that is positive definite and decrescent with respect to part of the system state, which can clearly be seen to be the solution to the steady‐state form of the Hamilton–Jacobi–Bellman equation and hence guaranteeing both partial stability and optimality. The overall framework provides the foundation for extending optimal linear‐quadratic controller synthesis to nonlinear nonquadratic optimal partial‐state stabilization. Connections to optimal linear and nonlinear regulation for linear and nonlinear time‐varying systems with quadratic and nonlinear nonquadratic cost functionals are also provided. Finally, we also develop optimal feedback controllers for affine nonlinear systems using an inverse optimality framework tailored to the partial‐state stabilization problem and use this result to address polynomial and multilinear forms in the performance criterion. Copyright © 2015 John Wiley & Sons, Ltd.     

Random sampling approach to state estimation in switching environments

This paper deals with the state estimation for the systems under measurement noise whose mean and covariance change with Markov transition probabilities. The minimum variance estimate for the state involves consideration of a prohibitively large number of sequences, so that the usual computation method becomes impractical. In the algorithm proposed here, the estimate is calculated with a relatively small number of sequences sampled at random from the set of a large number of sequences. The average risk of the algorithm is shown to converge to the optimal average risk as the number of sampled sequences increases. An ideal sampling probability yielding a very fast convergence is found. The probability is approximated in a minimum mean squared sense by a probability according to which sequences can be sampled sequentially and with great ease. This policy of determination of sampling probability makes it possible to design practical and efficient algorithms. Digital simulation results show a good performance of the proposed algorithm.     

Operating optimality assessment based on optimality related variations and nonoptimal cause identification for industrial processes

In this study, a novel online operating optimality assessment based on optimality related variations and nonoptimal cause identification method is proposed for industrial processes. The optimality related variations are extracted from each steady performance grade by analyzing the common and unique variations among steady performance grades, which avoids the time-consuming data alignment. When the optimality related variations are used in assessment, both the robustness and sensitivity of the assessment method are improved compared with the PCA-based assessment for its abilities in highlighting the process variations related to operating performance and excluding those unrelated variations. Based on the similarities between the optimality related variations of the online data and that of each steady performance grade, the process operating performance can be evaluated as the steady performance grade or the conversion process between performance grades, and this provides more information for the in-depth understanding of the process operating. For nonoptimal operating performance, the nonoptimal cause identification strategy is developed for further production adjustment and performance improvement. Finally, the efficiency of the proposed method is illustrated with a case of gold hydrometallurgical process.     

On the global optimality of unbiased minimum-variance state estimation for systems with unknown inputs

In this paper, a globally optimal filtering framework is developed for unbiased minimum-variance state estimation for systems with unknown inputs that affect both the system state and the output. The resulting optimal filters are globally optimal within the unbiased minimum-variance filtering over all linear unbiased estimators. Globally optimal state estimators with or without output and/or input transformations are derived. Through the global optimality evaluation of this research, the performance degradation of the filter proposed by Darouach, Zasadzinski, and Boutayeb [Darouach, M., Zasadzinski, M., & Boutayeb, M. (2003). Extension of minimum variance estimation for systems with unknown inputs. Automatica, 39, 867-876] is clearly illustrated and the global optimality of the filter proposed by Gillijns and De Moor [Gillijns, S., & De Moor, B. (2007b). Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough. Automatica, 43, 934-937] is further verified. The relationship with the existing literature results is addressed. A unified approach to design a specific globally optimal state estimator that is based on the desired form of the distribution matrix from the unknown input to the output is also presented. A simulation example is given to illustrate the proposed results.     

一种基于RTSP协议的电信级VOD系统

介绍了流媒体技术和RTSP协议，并提出一种基于RTSP协议的电信级大容量高可靠性的VOD系统解决方案。这种VOD系统具有成本相对低廉，实现相对简单，容量大，服务稳定的特点。该方案的实现可为其他VOD系统的设计实现提供参考。     

A constructive approach for an optimal control applied to a class of nonlinear time delay systems

In this contribution, we obtain a nonlinear controller for a class of nonlinear time delay systems, by using the inverse optimality approach. We avoid the solution of the Hamilton Jacobi Bellman type equation and the determination of the Bellman's functional by extending the inverse optimality approach for delay free nonlinear systems to time delay nonlinear systems. This is achieved by combining the Control Lyapunov Function framework and Lyapunov-Krasovskii functionals of complete type. Explicit formulas for an optimal control are obtained. The efficiency of the proposed method is illustrated via experimental results applied to a dehydration process whose model includes a delayed state linear part and a delayed nonlinear part. To give evidence of the good performance of the proposed control law, experimental comparison against an industrial Proportional Integral Derivative controller and optimal linear controller. Additionally experimental robustness tests are presented.     

Appointment scheduling algorithm considering routine and urgent patients

This paper derives a solution approach to solve the outpatient appointment schedule problem for given numbers of routine and urgent patients considering a no-show probability to minimize the weighted sum of average patient wait time, physician idle time and overtime. An exact deterministic service time method is proposed to find the optimal schedule. An exponentially distributed service time property is presented to show that the objective function for routine and urgent patients is not multimodular, and consequently a local search algorithm based on multimodulary does not guarantee global optimality. Thus, a heuristic algorithm based on two kinds of shifting policies (HE-TKS) is developed to solve the appointment schedule, which gives a local optimal solution as an upper bound for the optimal schedule. Numerical experiments are conducted to illustrate how the critical factors affect service efficiency of the clinic in practice. It reveals that lower no-show probability, smaller interval lengths, shorter service times, and more urgent patients will benefit both patients and clinics.