移动视频业务QoE自适应测量工具

针对不同无线环境（3G、WiFi）下获取用户体验质量（Quality of Experience,QoE）数据困难和不精确的问题,提出一种基于安卓（Android）移动终端视频业务QoE的自适应测量方法.通过实时测量并评估用户在线视频业务体验质量,提高用户体验质量评价的准确性和实用性.为此开发了能自动测量视频QoE的工具,测量服务质量（Quality of Service,QoS）客观参数,通过效用函数映射到主观QoE（MOS值）.通过对理论QoE评价模型（取自文献）与用户实际反馈相关性研究改进理论模型.结果表明,无线环境下改进的模型测量结果更接近用户实际反馈,可以更好地评价QoE.     

流量业务用户感知模型建立及工程化应用

用户感知是终端用户对设备、网络和系统、应用或业务的质量和性能（包括有效性和可用性等方面）的综合主观感受,是终端用户对移动网络提供的业务性能主观感受.它以接近量化的方法来表示终端用户对业务与网络的体验和感受.本文通过层次分析法,建立QoE的评估模型.给出QoE评分的数学公式.并通过一个实例,示范评估过程.     

网络传输中IPTV的QoE评估模型的研究

IPTV是一种基于Internet的多媒体网络服务,由于Internet本身的不可靠性,使其在网络传输过程中无法保证服务质量。为了实时定量评估IPTV服务质量,提出了一种基于IPTV的用户体验评估模型,通过建立从网络服务质量QoS到用户体验质量QoE的映射关系,借助QoS测量技术,以实现针对QoE的在线评估。实验建立IPTV仿真平台,模拟真实网络环境下IPTV媒体流传输的整个过程,实现网络损伤QoS可控和QoE可测。针对不同编码和不同内容的视音频,分别建立独立的QoE评估模型。同时考虑到模型对数据精度和计算速度的需求,给出优化的QoE评估模型。实验结果表明,该评估模型与实际用户体验具有较高的拟合度。     

IP网络中视频流QoE评价模型

网络视频业务的兴起使网络运营商和服务提供商更加关注视频的用户体验（QoE）,然而视频用户体验（QoE）值由于其主观性且评价过程复杂,难以在视频流传输中实时获取。通过实验分析了视频传输过程中服务质量（QoS）参数变化对视频QoE的影响,建立了客观、可测量的QoS参数与视频QoE之间映射模型,用可量化的QoS参数来评定视频QoE受网络性能的影响程度,以评估网络视频质量,该模型形式简单,能够实时监测视频质量。实验结果表明,该模型的评价结果能较好反映视频QoE。     

MMS业务的QoE定义与测量

用户体验质量已成为移动通信中的一个热门话题,为了有效地测量多媒体短信业务（MMS）的用户体验质量,分析了QoE的定义以及它与传统QoS的区别,介绍了QoE的KQI和KPI之间的相互关系并建立起QoE的映射模型;然后,结合MMS业务的实现流程以及所涉及的网元设备详细定义了MMS业务中的KQIs与KPIs,最后,针对MMS业务提出了一种基于统计样本的QoE测量方案,通过测量明确KPIs与QoE之间的关系以及影响用户体验质量的因素,这对于提高用户的体验质量有重要意义。     

基于用户体验延迟的QoE层次评估模型

针对Web服务中难以获得反映用户体验的数据,且难以对用户体验质量（QoE）进行评估的问题,在分析了Web数据和现有评价方法的基础上,提出了一种融入用户体验延迟ED的QoE层次评价方法。该方法基于层次分析法,并结合人类生理主观感受,能够更加客观地衡量用户体验。通过相关案例验证了该方法的可行性。     

基于双向强化学习与动态码率调节的无线mesh网络协议

针对现有无线mesh网络协议的用户体验质量(QoE)较差的问题,提出一种基于双向强化学习与动态码率调节的无线mesh网络协议。首先,设计了兼容不同服务类型的无线mesh网络QoE度量框架;然后,设计了基于双向强化学习的无线mesh网络路由协议;最终,结合QoE感知的差异化报文调度策略与数据流源节点码率动态调节算法进一步优化终端用户的QoE质量。基于NS-2仿真平台的对比实验结果显示,本协议可明显地提高无线mesh网络的QoE指标,同时具有较低的控制开销。     

基于QoS量化用户体验质量的评价模型

针对网络环境中业务供应商提供的业务不能很好的满足用户需求的问题,引入用户体验质量（QoE）并结合服务质量（QoS）参数,通过仿真网络得到相应数据,使用matlab工具分别建立用户体验质量与比特率、用户体验质量与丢包率的评价模型.运用统计分析方法对评价模型进行数据分析,并与史蒂文斯幂定律、韦伯-费希纳定律等模型进行对比验证.结果表明,构建的评价模型能更精确的体现用户体验质量与服务质量的关系,为供应商提供了重要的参考依据,并指明了服务方向.     

服务功能链中基于机器学习的QoE评估与预测

在软件定义网络与网络功能虚拟化协同的网络架构下,只考虑单个服务质量（QoS）指标的服务功能链部署无法满足用户的多业务体验需求。提出一种基于机器学习的服务功能链部署模型。基于层次分析法构造MPNQ2算法以建立QoS与体验质量（QoE）的映射关系,得出影响QoE的网络参数并评估其影响权重。在此基础上,利用具备较强综合学习和泛化能力的随机森林模型对服务功能链的QoE进行预测。实验结果表明,与梯度提升决策树、线性判别分析等机器学习模型相比,随机森林模型为预测QoE的最佳模型,同时在影响QoE的网络参数中,丢包率对服务功能链的部署影响最大。     

网络传输公平性测量与算法设计：视频应用案例

算网融合以计算为中心、网络为根基,通过网络连接异构计算节点,实现算网资源的高效分配与调度.关于竞争流之间资源共享的公平性问题是算网融合的重要研究方向.作为算网融合的典型场景,视频应用正变得越来越重要,但人们对于它们是否以及在多大程度上遵守公平性原则知之甚少.在高度多样化的网络环境和缺乏自动化测量工具的情况下,公平性测量研究面临着巨大的挑战.通过测量典型视频应用Zoom的竞争行为来研究这个问题发现,资源竞争行为是复杂多变的,Zoom在不同的场景下有着不同的资源抢占行为.为了深入理解这些竞争行为,开发了自动化工具并进行测量以了解其用户体验（QoE）指标,包括端到端视频/音频时延、视频帧率和视频质量等. Zoom使用抢占带宽的策略来保证自身应用的用户体验.为了追求更好的用户体验,Zoom往往会自私地发送过多的冗余数据包来应对异常的网络情况,其中一些是不必要的.为此,设计一种能够在用户体验和公平性目标之间取得平衡的传输算法是非常重要的.提出了算法QLibra,并通过实验证明它可以有效保障上层应用的用户体验并且对竞争流无害.     

User feature-aware trustworthiness measurement of cloud services via evidence synthesis for potential users

Cloud computing can provide elastic and dynamic resources on demand, which facilitates service providers to make profits resulting from the long tail effect. It becomes vitally important to ensure that cloud services can be acceptable to more potential users. However, it is challenging for potential users to discover the trustworthy cloud services due to the deficiency of usage experiences and the information overload of QoE (quality of experience) evaluations from consumers. This paper presents a user feature-aware trustworthiness measurement approach for potential users. In this approach, the influence factors of QoE are systematically analyzed based on the user feature model and the quantitative computation methods are designed to measure the user feature similarity. In addition, employing FAHP (fuzzy analytic hierarchy process) method identifies the user feature community. To enhance the accuracy of trustworthiness measurement, the false evidences in QoE evaluations are iteratively filtered out with dynamic mean distance threshold. Finally, the trustworthiness of service is measured via evidence synthesis combining user feature similarity. The experiments show that this approach is effective to improve the quality of trustworthiness measurement, which is helpful to solve information overload problem and cold start problem of trusted service recommendation for potential users.     

Recommend trustworthy services using interval numbers of four parameters via cloud model for potential users

How to discover the trustworthy services is a challenge for potential users because of the deficiency of usage experiences and the information overload of QoE (quality of experience) evaluations from consumers. Aiming to the limitations of traditional interval numbers in measuring the trustworthiness of service, this paper proposed a novel service recommendation approach using the interval numbers of four parameters (INF) for potential users. In this approach, a trustworthiness cloud model was established to identify the eigenvalue of INF via backward cloud generator, and a new formula of INF possibility degree based on geometrical analysis was presented to ensure the high calculation precision. In order to select the highly valuable QoE evaluations, the similarity of client-side feature between potential user and consumers was calculated, and the multi-attributes trustworthiness values were aggregated into INF by the fuzzy analytic hierarchy process method. On the basis of ranking INF, the sort values of trustworthiness of candidate services were obtained, and the trustworthy services were chosen to recommend to potential user. The experiments based on a realworld dataset showed that it can improve the recommendation accuracy of trustworthy services compared to other approaches, which contributes to solving cold start and information overload problem in service recommendation.     

一种视频质量监测系统的设计与实现

网络服务提供商希望能从用户的角度了解目前网络所提供的服务质量,而用户也希望获得定量的指标来评价当前网络服务质量。为此,以视频质量监测为研究对象,提出一种面向用户体验质量的网络监测系统。通过实验分析了网络传输过程中QoS参数对视频QoE的影响;提出一种将视频流转化为测试序列的视频丢包测量方法,该方法能低入侵、准确测量视频传输过程中的丢包情况;基于以上的研究成果,通过对MIB库的扩展和对MIB库轮询机制的研究,构建了面向QoE的视频服务监测系统,该监测系统结构简单、可行性强,实验表明可实时对网络中的视频服务质量进行监测。     

IaaS云安全研究综述

目前,在新一代大规模互联网迅猛发展的背景下,产生的数据量也随之持续增长,这就导致用户的本地设备难以满足海量数据的存储和计算需求。与此同时,云计算作为一种经济高效且灵活的模式,具有易于使用、随用随付、不受时间和空间限制的优势,彻底改变了传统IT基础设施的提供和支付方式,可以有效解决无限增长的海量信息存储和计算问题。因此,在没有昂贵的存储成本和计算资源消耗的情况下,资源有限的用户可以采用云服务提供商（CloudServiceProvider,CSP）为用户提供所期望的服务。其中,基础设施即服务（Infrastructure as a Service, IaaS）作为云计算的三种服务类型之一,将虚拟化、分布式计算和网络存储等技术结合,可以在互联网上提供和租用计算基础设施资源服务（如计算、存储和网络）。故云计算依靠Iaa S层提供的计算基础设施资源,使用户不再需要购买额外设备,从而大大降低使用成本,同时也为上层服务奠定基础。然而,随着云计算服务的不断发展,基于IaaS的安全问题引起人们的关注。为了系统了解Iaa S的安全研究进展和现状,本文对IaaS的安全问题以及学术界和工业界的解决方案进行了...     

Keeping the user in the cloud: a cognitive social capital antecedent to use continuance and trust-commitment in personal cloud storage services

While personal cloud storage services are popular, providers experience difficulty retaining users. Two explanations of user continuance intention, being use continuance theory and trust-commitment theory, emerge from prior literature. However, there is no unifying theoretical lens to reconcile these two explanations. We use a cognitive social capital explanation to unify these two literature streams. We apply our research model to 237 cloud storage users. We find that the cognitive social capital lens significantly predicts both use continuance intention and trust-commitment in personal cloud storage services. Implications for cloud service providers are offered.     

User continuance intention to use cloud storage service

Cloud computing is a huge and important change in the field of network application in recent years to provide users with a completely different IT service and delivery mode. Among various cloud services, cloud storage is a service most closely related to web users’ need because it involves the storage of users’ all important data and backup files. In this study, a sample survey was conducted in Taiwan, and key factors influencing individual users’ adoption of the cloud storage service were analyzed and discussed based on Task-Technology Fit theory. The research results indicate that “cloud storage service”, “unstructured task”, “cloud storage self-efficacy” and “opinion of reference groups” all have significant positive influences on the “perceived usefulness”, which further has influence on users’ continuance intention to use the cloud storage service. The findings also support that the privacy protection risk and the lack of privacy-policy risk in the cloud storage service produce negative moderating effects on the perceived usefulness and the continuance intention.     

Understanding Service Quality and System Quality Success Factors in Cloud Archiving From an End-User Perspective

This study seeks to explain the adoption of cloud storage services as a means of personal archiving thereby focusing on users’ service and system quality perceptions and their drivers. The authors derive and empirically validate a model that incorporates users’ perceptions of service/system quality as well as behavioral factors to explain usage. Finally, the authors highlight important determinants of system/service quality perceptions that cloud providers should pay attention to in their attempts to increase marketshare.