摘要
云计算技术与移动互联网相结合产生了移动云计算。移动云计算以移动网络为传输介质,以移动智能终端为用户终端,通过弹性的提供可扩展的服务,使用户获取硬件、软件、平台等IT资源。随着云计算这几年的发展,它己成为全球前沿信息通信领域的热点。随着3G乃至4G时代的到来,平板电脑、手机等智能终端的渗透,越来越多的用户倾向于通过移动云计算来满足工作与生活的需要,所以移动云计算的发展会带来很多商机。
作为移动云计算提供商,为创造最大化的利益,必须全面了解移动云计算用户的使用体验现状及需求,从而有针对性地进行移动云计算服务能力提升。本研究站在移动云计算提供商角度,分用户群(个人用户/企业用户)深入研究移动云计算的QoE (Quality of Experience)评价的影响因素,分析因素之间的相互关系,并针对核心影响因素进行移动云计算的QoE优化,有利于云提供商对移动云计算业务的管理和营销,对推动移动云计算稳定快速的发展有非常重大的理论和现实意义。
本研究主要由以下几部分组成：首先阐述国内外移动云计算发展现状,说明研究意义及对象,并介绍研究方案与创新之处；其次,查阅整理国内外学术界与云计算、QoE、优化相关的文献资料,并对文献进行深入分析总结；第三,构建移动云计算的个人用户QoE评价的影响因素模型,设计量表并通过预调研和正式调研收集数据,通过信度分析、效度分析、因子分析和相关分析对数据进行分析,最后通过结构方程模型验证假设,从而找出移动云计算的个人用户QoE评价的核心影响因素；第四,采用层次分析法并结合企业用户特点,构建移动云计算的企业用户QoE评价模型,开发量表并收集数据对概念模型进行实证分析,通过模糊综合评判法找出移动云计算的企业用户QoE评价的核心指标；第五,将上述研究结论作为输入,采用多目标优化方法,针对移动云计算个人用户和企业用户分别构建QoE优化模型并选择实际算例进行验证。试图使移动云计算提供商能够用最合理的成本分配达到最大的用户QoE收益。这将平衡用户与云提供商双方的利益问题,使云提供商能够向个人用户和企业用户提供最好的用户感知与用户体验。
本研究按照Wallance的科学研究方法论,首先介绍研究背景,然后提出研究问题与目的,并说明研究对象。然后基于现有的QoE研究理论,定义相关概念并提出研究假设。通过绩效期望、努力期望、社会影响、促成条件、感知价值、感知利益、感知成本、感知风险等概念与移动云计算的个人用户QoE评价建立联系,通过调研收集数据,使用SPSS进行数据分析,使用AMOS检验研究假设。通过功能体验、技术体验、过程体验、人性化体验、结果体验与移动云计算的企业用户QoE评价建立联系,接着通过调研收集数据,并用层次分析法、模糊综合评判法分析数据。最后,针对上述实证分析的结论建立移动云计算的QoE多目标优化模型,并通过Matlab、LINGO软件对优化模型进行算例验证。通过上述研究过程,可验证理论及模型的正确与否,进而指出未来研究方向。
通过理论和实证分析,本研究得出以下结论：
第一,移动云计算的个人用户QoE评价的影响因素模型的检验结论。本模型自变量的解释力度为61.884%,因变量的解释力度为91.752%。绩效期望、努力期望、社会影响、促成条件、感知价值对于QoE评价影响均为显著(P<0.001),且为正相关。感知利益、感知成本、感知风险对于感知价值的影响均为显著(P<0.001),其中感知成本和感知风险对感知价值为负相关,感知利益为正相关。通过结构方程验证,得出结论,移动云计算的个人用户QoE评价中,正面核心影响因素为“绩效期望”、“社会影响”；负面核心影响因素为“感知成本”、“感知风险”。
第二,移动云计算的企业用户QoE评价模型的检验结论。本研究构建了移动云计算企业用户QoE评价的四层指标体系,根据17位专家的权重调查问卷设置各指标权重；然后根据65份打分问卷的结果,结合层次分析法计算出的权重,综合考虑多种因素的作用,利用模糊综合评判法对移动云计算的企业用户QoE做出综合评价。从评价结果可以看出,移动云计算的企业用户QoE评价的满意度为“比较满意”,且满意度较高(比例为92%)。其中,QoE评价较高的一级指标为“功能体验”和“人性化体验”；QoE评价较低的一级指标为“过程体验”和“结果体验”。QoE评价较高的二级指标为“共享性”、“简易性”、“稳定性”、“外部影响”和“美学感受性”；QoE评价较低的二级指标为“安全性”、“连接有效性”和“内部影响”。QoE评价较高的三级指标分别为“提供的应用具有共享性”、“提供的信息具有共享性”、“移动云计算服务可持续稳定使用性”、“同步协作准确性”；QoE评价的较低的三级指标为“工作机密安全性”、“用户信息安全性”、“人力资源成本节约度”、“工作绩效提升度”。得出结论,移动云计算提供商应重点关注企业用户QOE评价的指标为“工作机密安全性”、“用户信息安全性”、“人力资源成本节约度”、“工作绩效提升度”。
第三,移动云计算的个人用户QoE优化模型的检验结论。本研究基于多目标优化理论,从用户感知成本降低度、用户感知风险降低度、用户绩效期望提升度、社会影响提升度四个方面进行目标函数设计。选择有道云笔记作为算例,得出结论,网易旗下的有道公司在对有道云笔记进行投资决策时,应当将18.8%的资金用于宣传,以提升用户的社会影响目标；52.6%的资金用于数据处理能力的技术革新,以提升用户的绩效期望,降低用户感知成本；剩余28.6%的资金用于安全保护能力的技术革新,通过防止用户信息泄露等一系列手段降低用户使用有道云笔记的感知风险。
第四,移动云计算的企业用户QoE优化模型的检验结论。本研究基于多目标优化理论,从信息资料安全性、人力资源成本节约度、工作绩效提升度三个方面进行目标函数的设计。选择IBM的TAP(Technology Adoption Program)项目作为算例,得出结论,IBM在对TAP项目进行投资决策时,应当将42.5%的资金用于信息资料安全技术的提升；38.2%的资金用于处理能力的优化,以提升工作绩效；剩余19.3%的资金作为节约人力资本的软硬件资金投入。
In recent years, cloud computing has become a global field of information, especially in the frontier areas such as communication, computer, software and network. After several years of development, cloud computing has gone through the fuzzy stage, and becomes more focused and pragmatic, which is expected to start the stage of rapid development. Some innovative Internet companies use cloud platforms for low-cost and fast-response. Cloud computing reduces system deployment costs and operating expenses greatly, speeds up business online, which formes a huge industry scale, the enterprise value and social impact gradually.
At the same time, users are more inclined to use the personalized mobile terminals such as smart phone, tablet PC and iTV etc.Through Internet and cloud computing, communications can occur between man and man, man and things, things and things. With the penetration of smart terminals, and the coming of3G and even4G era, the combination of cloud computing and mobile Internet will generate more business opportunities. Researching on the quality of user experience impact factors of mobile cloud computing, and optimizing mobile cloud computing user experience, which is conducive to the provider of mobile cloud computing business management and marketing.The study enhances steady and rapid development of mobile cloud computing business in China, and it has important theoretical and practical significance.
This study includes five sections:Firstly we introduce business development and research background of mobile cloud computing, define the scope of research, and proposes the scientific problems.Secondly, we review the literature about cloud computing, QoE(quality of user experience),and optimization.Thirdly,we build the influence factors model of individual user QoE evaluation about mobile cloud computing, develop scale, collect data,and do empirical analysis. Fourthly, we build the model of business users QoE evaluation about mobile cloud computing, develop scale, collect data,and do empirical analysis.Finally, using the conclusion of the third and fourth part as input, we build individual user/business users QoE optimization model of mobile cloud computing respectively,and select actual examples to verify the analysis.
This study applied the research methodology proposed by Wallance. Firstly, after expounding mobile cloud computing development status at home and abroad, we indicate the research significance and object, and describe the research programs and innovation. Then, based on the existing QoE theory, profiling and defining the relevant concepts and put forward the relationship between the reaction concept assumptions.we establish contact between mobile cloud computing individual user QoE evaluation and concepts such as performance expectancy, effort expectancy, social influence, enabling conditions, perceived value, perceived benefits, perceived cost, perceived risk. we establish contact between mobile cloud computing business users QoE evaluation and concepts such as functional experience, technology experience,process experience, human experience, result experience.After collecting data from the social survey, we use SPSS and AMOS to analyze data and validate assumptions.Finally, we build the QoE optimization model of mobile cloud computing, and Validate of the model by the actual data, in order to verify the correctness of the theory and triggered future research problems.
After the theoretical and empirical analysis, we draw the following conclusions:
(i) The conclusions of the influence factors model of individual user QoE evaluation about mobile cloud computing.In this study, each variable Kroner Bach alpha coefficient is above0.7, the overall reliability is0.886, the CITC values of most factor measurement projects are greater than0.3. The overall KMO test coefficient of the sample data is0.881, and the sample distribution spherical Bartlett test statistical significance is0.000. By factor analysis of the independent variables, the independent variables are automatically classified into seven factors, and the contribution rate is61.884%,which means strong interpretation strength. Due to the variable factor analysis, seven problems of dependent variables are classified into two factors, and the contribution rate is91.752%.The impacts for QoE evaluation of performance expectancy, effort expectancy, social impact, enabling conditions, perceived value are significantly (P<0.001) and positive. The impacts for perceived value of perceived benefits, perceived cost, perceived risk are significantly (P <0.001),and the impacts for perceived value of perceived cost, perceived risk are negative.Come to the conclusion, the positive core impact factors are "performance expectations"(path value R=0.44) and "social impact"(path value R=0.33); the negative core impact factors are "perceived cost"(path value R=-0.70)" and "perceived risk"(path to value R=-0.30).
(ii) The conclusions of the model of business users QoE evaluation about mobile cloud computing.In this study, we set index weights by survey questionnaire which17experts filled in.According to the results of the65questionnaire,we use of fuzzy comprehensive evaluation method to make a comprehensive evaluation of mobile cloud computing individual user QoE. As can be seen from the results of the evaluation, the evaluation of mobile cloud computing individual user QoE is"quite satisfactory", and the degree of satisfaction is high (92%).But there still exist some short board of individual user QoE evaluation,such as" process of experience"(no satisfaction is15%)and "result experience"(no satisfaction is12%).They are embodied in"the security of work"," the security of user information"," human resources cost savings"and" work performance improvement".
(iii) The conclusions of the individual user QoE optimization model of mobile cloud computing. In this study, we design the objective functions from "performance expectations","social impact","perceived cost" and "perceived risk".We select Youdao cloud notes as the example,concluded that when the Youdao company make invest decision to Youdao cloud notes,it should use18.8%of the funds for publicity,so as to enhance the individual user's social impact goals.It should use52.6%of the funds for technological innovation of data processing capabilities,so as to enhance the user's performance expectations,and reduce the cost of user perception. It should use28.6%of the funds for technological innovation of ecurity capabilities,so as to reduce perceived risk.According to the multi-objective optimization model,we can improve the individual user QoE by changing the funds invested proportion of provider,so as to balance the interests of providers and users,then achieve a win-win.
(iv) The conclusions of the business users QoE optimization model of mobile cloud computing.In this study, we design the objective functions from"the security of information"," human resources cost savings"and" work performance improvement".We select IBM TAP (Technology Adoption Program) project as example,concluded that when IBM company make invest decision to TAP project,it should use42.5%of the funds for the enhancement of information security technology. It should use38.2%of the funds for optimization of the processing power to enhance job performance. It should use19.3%of the funds for hardware and software to save human capital investment. According to the multi-objective optimization model,we can improve the business users QoE by changing the funds invested proportion of provider,so as to balance the interests of providers and users,then achieve a win-win.
引文
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