流媒体系统基于用户行为分析的资源管理研究
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摘要
随着通信技术和互联网的发展,视频点播服务(Video on Demand, VoD)以其访问便捷、内容丰富等特征越来越受到人们的关注并得到了飞速发展,成为互联网上的主流应用。为应对庞大的用户规模、海量的数据存储和动态的用户交互请求,内容分发网络和对等网络等构建于物理网络之上的逻辑覆盖网络被用于流媒体系统的开发,以提高系统的吞吐量和可扩展性。用户在视频访问过程中的交互式操作和分布式系统中节点及资源的动态变化都制约了流媒体服务的服务质量,研究流媒体中的用户行为模型和资源管理策略,对提供高质量的流媒体服务具有重要的现实意义。
本文以国家863项目“融合网络业务体系的开发”课题和国家科技支撑计划“支持跨区域、多运营商的新一代广播电视服务系统”课题为研究背景,以提高系统资源利用效率、保障流媒体服务的服务质量为目的,在多媒体覆盖网络中研究系统的资源管理和服务组合策略,通过对用户在流媒体播放过程中的交互式操作行为的统计建模,实现高性能的流媒体数据缓存预取策略。本文主要研究内容包括以下三个方面：
(1)提出一种保障QoS分布式流媒体系统资源管理策略
针对动态分布式环境下服务节点资源分配和流媒体服务的QoS保障等问题,在传统的内容分发网络的基础上提出采用多媒体覆盖网整合和管理分布式系统中的资源,并采用具有QoS保证的服务组合算法向用户提供具有QoS偏好的流媒体服务。通过对系统中各种资源的分析及服务质量各个属性的定义,提出了元服务QoS模型以及组合服务的QoS计算方法,在给出具有服务质量保证的服务组合问题的数学模型的基础上,提出基于学习自动机理论的服务组合算法。该算法通过对服务覆盖网中资源与服务状况的感知与学习,寻找满足资源约束条件与用户给定服务质量的服务组合策略,在实验设定由5-90个元服务构成的组合服务时,使用该学习算法可以在多项式时间内选择出最优或次优的服务组合策略,并具有良好的可扩展性。
(2)建立基于隐马尔可夫模型的用户交互式行为模型
建立和分析用户的交互行为模型可以对系统的性能进行有针对性的优化,以提高服务效率。本文在统计和分析用户观看视频过程中的交互行为的基础上,将用户观看行为建模为隐马尔可夫模型,并在所建立模型的基础上对用户浏览状态进行预测,该策略使用Baum-Welch法对隐状态的转移概率参数进行极大似然估计,并利用系统参数的部分先验信息,避免所估计参数的似然函数陷入局部极大值,然后根据系统参数和单个用户的在线操作行为更新用户浏览状态的后验概率,使用最大后验概率准则对用户行为进行判决。通过贝叶斯推理,跟踪用户浏览状态的变化,充分利用了HMM模型的先验知识和当前用户记录在流媒体服务器上的操作信息,尤其是对于热门视频的观看行为,得益于用户在观看热门视频过程中丰富的VCR操作,对用户的交互操作预测准确率能达到77.5%以上,该策略具有较为明显的建模能力。
(3)提出一种基于模型预测的流媒体预取策略
本文综合计算了用户在视频访问过程中的初始访问延迟以及视频对象在播放过程中因无法及时获得所需数据而导致的网络抖动延迟,结合媒体对象各数据段的初始访问概率和条件访问概率提出了降低延迟的优化公式。根据模型参数和用户当前所处状态对用户下一时刻可能访问的数据段进行判决,由判决结果计算影片数据段的预取价值和缓存价值并实施预取策略以降低用户访问视频过程中的延迟和抖动。实验表明,本算法针对交互操作频繁的热门视频,采用预取技术能充分利用系统的带宽并及时获得用户将要访问的数据从而降低访问延迟发生的概率,提高流媒体系统的服务质量,其延时降低量比采用简单统计的预取算法高出6%左右,比单纯采用LRU策略的缓存算法高出20%。
With the development of communication technology and the Internet, video-on-demand services (Video on Demand, VoD) has got lots of attention for its convenient access to rich content. VoD has boomed recently and become mainstream applications on the Internet. In response to large-scale users, mass data storage and dynamic user interaction request, the logical overlay network such as content distribution network and Peer-to-Peer network is built on top of the physical network to improve system throughput and scalability. The interactive behavior of users and the dynamics of resources in distributed systems are restricting service quality of streaming media services, the user behavior model and resource management strategies has important practical significance to provide high-quality streaming media Services.
Our work in this dissertation is based on the National863Project "the development of integration network scope" and the National Science and Technology Support Program "new generation of television service system supporting cross-regional and multi-service-providers". In the background of multimedia overlay network, in order to improve the service performance of video streaming application, we mainly discuss the resource management, the user behavior during playback of streaming media and high-performance media data prefetching strategies. The main contributions are given as follows:
(1) QoS guarantee for streaming media services in a distributed environment is a challenging task. On the basis of the traditional content distribution networks, Using of multimedia overlay network is an effective method for the integration and management of distributed systems resources. Through the analysis of various resources, we present a generic QoS model of service composition, as well as an algorithm to the QoS-aware service problem. The proposed algorithm is designed and implemented based on the concepts of learning automation. It has been rigorously tested and evaluated through extensive simulations. Our results show that the learning automation based approach is scalable and can effectively achieve service composition for QoS requirement satisfaction in polynomial time.
(2) User interactive behavior is the foundation and key technology of streaming system. Hidden Markov model was used to model the correlation in interactive behavior. The user viewing behavior is modeled as a hidden Markov model. The strategy estimated the hidden Markov model system parameters by utilizing a maximum likelihood estimation method called Baum-Welch algorithm. The posterior probability of the current user browsing state was updated by Bayesian inference, which was based on hidden Markov model and deduced from the posterior probability of the previous browsing state. Finally, the user browsing state was estimated according to the maximum posteriori criterion. Thanks to the use of the rich VCR operations in popular videos, the prediction accuracy rate can reach more than77.5%and the strategy has obvious modeling capabilities.
(3) In this paper, we derive the expectation of the start delays during the visit as well as network jitter delay during playback and propose and study the caching policy of proxy cache. The user experience with the traditional caching algorithms is bad if most of the clients'operations are randomized. We define the initial access probability of media data and the demanding transfer probability between two segments. According to the current model parameters and the user state, we judge the segment which may be accessed the next time and prefetch it to reduce the user access delay. Experimental results show that the algorithm using prefetching techniques can fully utilize the bandwidth of the system and can effectively reduce the probability of access latency, especially in the case of popular video access. Compared with the prefetching algorithms using only simple statistics and LRU cache strategies, the access delay was reduced by6%and20%respectively.
本文以国家863项目“融合网络业务体系的开发”课题和国家科技支撑计划“支持跨区域、多运营商的新一代广播电视服务系统”课题为研究背景,以提高系统资源利用效率、保障流媒体服务的服务质量为目的,在多媒体覆盖网络中研究系统的资源管理和服务组合策略,通过对用户在流媒体播放过程中的交互式操作行为的统计建模,实现高性能的流媒体数据缓存预取策略。本文主要研究内容包括以下三个方面：
(1)提出一种保障QoS分布式流媒体系统资源管理策略
针对动态分布式环境下服务节点资源分配和流媒体服务的QoS保障等问题,在传统的内容分发网络的基础上提出采用多媒体覆盖网整合和管理分布式系统中的资源,并采用具有QoS保证的服务组合算法向用户提供具有QoS偏好的流媒体服务。通过对系统中各种资源的分析及服务质量各个属性的定义,提出了元服务QoS模型以及组合服务的QoS计算方法,在给出具有服务质量保证的服务组合问题的数学模型的基础上,提出基于学习自动机理论的服务组合算法。该算法通过对服务覆盖网中资源与服务状况的感知与学习,寻找满足资源约束条件与用户给定服务质量的服务组合策略,在实验设定由5-90个元服务构成的组合服务时,使用该学习算法可以在多项式时间内选择出最优或次优的服务组合策略,并具有良好的可扩展性。
(2)建立基于隐马尔可夫模型的用户交互式行为模型
建立和分析用户的交互行为模型可以对系统的性能进行有针对性的优化,以提高服务效率。本文在统计和分析用户观看视频过程中的交互行为的基础上,将用户观看行为建模为隐马尔可夫模型,并在所建立模型的基础上对用户浏览状态进行预测,该策略使用Baum-Welch法对隐状态的转移概率参数进行极大似然估计,并利用系统参数的部分先验信息,避免所估计参数的似然函数陷入局部极大值,然后根据系统参数和单个用户的在线操作行为更新用户浏览状态的后验概率,使用最大后验概率准则对用户行为进行判决。通过贝叶斯推理,跟踪用户浏览状态的变化,充分利用了HMM模型的先验知识和当前用户记录在流媒体服务器上的操作信息,尤其是对于热门视频的观看行为,得益于用户在观看热门视频过程中丰富的VCR操作,对用户的交互操作预测准确率能达到77.5%以上,该策略具有较为明显的建模能力。
(3)提出一种基于模型预测的流媒体预取策略
本文综合计算了用户在视频访问过程中的初始访问延迟以及视频对象在播放过程中因无法及时获得所需数据而导致的网络抖动延迟,结合媒体对象各数据段的初始访问概率和条件访问概率提出了降低延迟的优化公式。根据模型参数和用户当前所处状态对用户下一时刻可能访问的数据段进行判决,由判决结果计算影片数据段的预取价值和缓存价值并实施预取策略以降低用户访问视频过程中的延迟和抖动。实验表明,本算法针对交互操作频繁的热门视频,采用预取技术能充分利用系统的带宽并及时获得用户将要访问的数据从而降低访问延迟发生的概率,提高流媒体系统的服务质量,其延时降低量比采用简单统计的预取算法高出6%左右,比单纯采用LRU策略的缓存算法高出20%。
With the development of communication technology and the Internet, video-on-demand services (Video on Demand, VoD) has got lots of attention for its convenient access to rich content. VoD has boomed recently and become mainstream applications on the Internet. In response to large-scale users, mass data storage and dynamic user interaction request, the logical overlay network such as content distribution network and Peer-to-Peer network is built on top of the physical network to improve system throughput and scalability. The interactive behavior of users and the dynamics of resources in distributed systems are restricting service quality of streaming media services, the user behavior model and resource management strategies has important practical significance to provide high-quality streaming media Services.
Our work in this dissertation is based on the National863Project "the development of integration network scope" and the National Science and Technology Support Program "new generation of television service system supporting cross-regional and multi-service-providers". In the background of multimedia overlay network, in order to improve the service performance of video streaming application, we mainly discuss the resource management, the user behavior during playback of streaming media and high-performance media data prefetching strategies. The main contributions are given as follows:
(1) QoS guarantee for streaming media services in a distributed environment is a challenging task. On the basis of the traditional content distribution networks, Using of multimedia overlay network is an effective method for the integration and management of distributed systems resources. Through the analysis of various resources, we present a generic QoS model of service composition, as well as an algorithm to the QoS-aware service problem. The proposed algorithm is designed and implemented based on the concepts of learning automation. It has been rigorously tested and evaluated through extensive simulations. Our results show that the learning automation based approach is scalable and can effectively achieve service composition for QoS requirement satisfaction in polynomial time.
(2) User interactive behavior is the foundation and key technology of streaming system. Hidden Markov model was used to model the correlation in interactive behavior. The user viewing behavior is modeled as a hidden Markov model. The strategy estimated the hidden Markov model system parameters by utilizing a maximum likelihood estimation method called Baum-Welch algorithm. The posterior probability of the current user browsing state was updated by Bayesian inference, which was based on hidden Markov model and deduced from the posterior probability of the previous browsing state. Finally, the user browsing state was estimated according to the maximum posteriori criterion. Thanks to the use of the rich VCR operations in popular videos, the prediction accuracy rate can reach more than77.5%and the strategy has obvious modeling capabilities.
(3) In this paper, we derive the expectation of the start delays during the visit as well as network jitter delay during playback and propose and study the caching policy of proxy cache. The user experience with the traditional caching algorithms is bad if most of the clients'operations are randomized. We define the initial access probability of media data and the demanding transfer probability between two segments. According to the current model parameters and the user state, we judge the segment which may be accessed the next time and prefetch it to reduce the user access delay. Experimental results show that the algorithm using prefetching techniques can fully utilize the bandwidth of the system and can effectively reduce the probability of access latency, especially in the case of popular video access. Compared with the prefetching algorithms using only simple statistics and LRU cache strategies, the access delay was reduced by6%and20%respectively.
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