Optimization-based resource allocation for software as a service application in cloud computing

Software as a service (SaaS) is a software that is developed and hosted by the SaaS vendor. SaaS cloud provides software as services to the users through the internet. To provide good quality of service for the user, the SaaS relies on the resources leased from infrastructure as a service cloud providers. As the SaaS services rapidly expand their application scopes, it is important to optimize resource allocation in SaaS cloud. The paper presents optimization-based resource allocation approach for software as a service application in cloud. The paper uses optimization decomposition approach to solve cloud resource allocation for satisfying the cloud user’s needs and the profits of the cloud providers. The paper also proposes a SaaS cloud resource allocation algorithm. The experiments are designed to compare the performance of the proposed algorithm with other two related algorithms.     

软件Saas模式下安全风险策略探讨

近年来,将软件作为一种服务形式提供给客户的需求逐渐增加,其中最突出的就是形成软件即服务(Software as a Service,SaaS)模式。这种新模式的出现正是顺应了这个需求,用软件服务代替传统的软件产品销售,不仅可以使软件免于盗版的困扰,而且可以降低软件消费企业购买、构建和维护基础设施以及应用程序的成本和困难。但SaaS模式需要用户将数据存放在服务供应商提供的存储介质上,因而使得数据的专属性、可靠性和安全性受到一定程度的威胁。文章主要对当前SaaS模式下的网络安全隐患进行了分析,并探讨了SaaS模式下网络安全隐患的防范策略。     

Opportunities and risks of software-as-a-service: Findings from a survey of IT executives

IT providers have heralded software-as-a-service (SaaS) as an excellent complement to on-premises software addressing the shortcomings of previous on-demand software solutions such as application service provision (ASP). However, although some practitioners and academics emphasize the opportunities that SaaS offers companies, others already predict its decline due to the considerable risk involved in its deployment. Ours is the first study to analyze the opportunities and risks associated with adopting SaaS as perceived by IT executives at adopter and non-adopter firms. We first developed a research model grounded in an opportunity-risk framework, which is theoretically embedded in the theory of reasoned action. Subsequently, we analyzed the data collected through a survey of 349 IT executives at German companies. Our findings suggest that in respect to both SaaS adopters and non-adopters, security threats are the dominant factor influencing IT executives' overall risk perceptions. On the other hand, cost advantages are the strongest driver affecting IT executives' perceptions of SaaS opportunities. Furthermore, we find significant differences between adopters' and non-adopters' perceptions of specific SaaS risks and opportunities, such as performance and economic risks as well as quality improvements, and access to specialized resources. Our study provides relevant findings to improve companies' assessment of SaaS offerings. It also offers SaaS providers insights into the factors that should be prioritized or avoided when offering SaaS services to companies at different stages of their technology adoption lifecycle.     

Competitive Pricing Strategies for Software and SaaS Products

We study the pricing strategies for a software firm and an entrant software-as-a-service (SaaS) firm in two customer markets: the market composed of the incumbent’s past customers and the market of new customers. We build a game theoretical model to investigate how user costs and the quality differential between products affect firms’ pricing in different customer markets. Our findings show that it is not always optimal for the software firm to price discriminate between its old users and the new customers. The entrant firm would be better off acquiring only the new customers when the SaaS quality is sufficiently low.     

Produktdifferenzierung f��r?Software-as-a-Service-Anbieter

The market for the new provisioning type Software-as-a-Service (SaaS) has reached a significant size and still shows enormous growth rates. By varying size of SaaS products, providers can improve their market position and profits by successfully acting in the tension area of customer acquisition, pricing and costs. We first elaborate differences concerning product differentiation between classic software provisioning models and SaaS. Then, we introduce a micro-economic based decision model to maximize the return of a provider by finding an optimal granularity, i.e. by varying the size of services. This paper makes two contributions in this context: (1) it provides a conceptual foundation for product differentiation within the scope of SaaS and (2) it presents the first implementation of variable reproduction costs for web based software offers. The model is illustrated by a real world case with data from a SaaS provider.     

Cloud federation in a layered service model

We show how a layered Cloud service model of software (SaaS), platform (PaaS), and infrastructure (IaaS) leverages multiple independent Clouds by creating a federation among the providers. The layered architecture leads naturally to a design in which inter-Cloud federation takes place at each service layer, mediated by a broker specific to the concerns of the parties at that layer. Federation increases consumer value for and facilitates providing IT services as a commodity. This business model for the Cloud is consistent with broker mediated supply and service delivery chains in other commodity sectors such as finance and manufacturing. Concreteness is added to the federated Cloud model by considering how it works in delivering the Weather Research and Forecasting service (WRF) as SaaS using PaaS and IaaS support. WRF is used to illustrate the concepts of delegation and federation, the translation of service requirements between service layers, and inter-Cloud broker functions needed to achieve federation.     

Coordination mechanism of IT service supply chain: an economic perspective

Currently, there are two models of service delivery in the information technology (IT) arena: implementation agent and software-as-a-service (SaaS), and each model results in a different IT service supply chain (SSC) structure. When IT service is delivered via implementation agents, independent software vendors (ISV) form the core of the supply chain, while in SaaS chains, platform operators act as the core. It is important to clarify how to coordinate supply chain participants in their respective supply chains, because there is sparse research about supply chain coordination contracts in the IT service industry. This paper draws upon the theory of revenue sharing contracts and investigates IT SSC coordination mechanisms under both SaaS and implementation agent models. The analytical results indicate that a SaaS supply chain has an optimal IT service price that is only related to the variable costs (C _os ) of the SaaS platform operator, and so if the variable cost (C _os ) can be reduced to zero, perfect coordination would exist. In contrast, the implementation agent supply chain achieves perfect coordination when the variable cost of the implementation agent is equal to the average unit implementation fee. By comparing the two supply chain models and interpreting their respective coordination mechanisms, this study can further advance supply chain contract theory research.     

SaaS平台访问控制研究

Saas平台软件交付模式将应用软件以服务的形式提供给客户,可缩减硬件采购、系统管理上的开销。由于租户数据统一存储于服务提供商处,如何在维持较高资源利用率的同时保障租户的数据安全是一个挑战性问题。针对租户角色复杂、各租户数据共存而又独立访问的要求,结合基于角色的访问控制模型,构建了支持多租户、多角色、方便租户权限管理的SaaS平台的访问控制模型。和传统基于角色的访问控制模型相比,该模型增加了租户的概念,以租户为基本单元实施平台的访问控制,提高了SaaS平台访问控制的安全性和可管理性。分析了用户访问SaaS平台的具体流程,给出了模型的形式语言描述,实现了SaaS餐饮管理平台访问控制的数据库的物理模型,为SaaS平台开发提供参考。     

Determinants of choice of semantic web based Software as a Service: An integrative framework in the context of e-procurement and ERP

The ever increasing Internet bandwidth and the fast changing needs of businesses for effectiveness with the partners in the procurement chain and is leading organizations to adopt information systems infrastructures that are cost effective as well as flexible. The question seems to be: what is driving organizations to go in for Software as a Service (SaaS) based e-procurement and ERP, rather than the packaged model of software provisioning? Whereas there have been studies reporting technology, cost, quality, network externalities and process as the main variables in the utility function of the user, but most of the studies have modelled either one or two in the their models. The study is exploratory in nature and tries to identify, classify and rank dimensions affecting SaaS sourcing decisions. In this study, we developed an integrative framework to identify the determinants of choice of SaaS in the specific context of SaaS based e-procurement and ERP. The framework was then analyzed using the extended Analytic Hierarchy Process (AHP) method suggested by Liberatore (1987) and the relative importance and the weights of the criteria identified using data collected on 8 users and 9 service providers of SaaS based e-procurement and ERP. Although the analysis helped in identifying quality and costs as the two most important determinants of choice of SaaS based e-procurement and ERP, but the other criteria such as network externality benefits, technology and process were also found to be significant determinants of choice.     

A two-phased SEM-neural network approach for consumer preference analysis

A fundamental task in the design of consumer products is consumer preference analysis. The primary focus of this task is establishing a mapping relationship between product parameters/attributes and consumer preferences. The key to connect the consumer space and the design space are user perceptions of the product. Among the many existing methods, the Structural Equation Model (SEM) is one of the most used methods because it explains the causal relationship between the input and the output variables explicitly. However, the relationship obtained from the conventional SEM is linear, which is usually not the case in practice. Fortunately, the Artificial Neural Network (ANN) provides a new perspective for building nonlinear models because of its nonlinear nature. Therefore, a two-phased SEM-NN approach for consumer preference analysis is introduced for identifying and mapping how product attributes affecting the fulfillment of user perceptions and ultimately their preferences. In this model, the consumer preference analysis is conducted in two phases: influence path construction, and path coefficient revision. The proposed method can reserve the original SEM topology that reflects the causal relationship between variables while using the training algorithm of ANN to obtain more accurate path coefficients. This model could help the designers to identify and map how product attributes affecting the consumer preferences, and to better understand the factors that affect user perceptions and the inner relationships between them. To demonstrate effectiveness of the model, a case study of smartphone is presented. It is shown that the SEM-NN model can make full use of the causal analysis of SEM and the nonlinear nature of ANN and ultimately provides more reliable results of consumer preference analysis.     

HyDB:集成MapReduce和数据库的高效SaaS架构

随着数据的快速增长和云计算的兴起,软件作为服务(SaaS)标志着计算机系统按需服务的应用的兴起.高效经济SaaS使得许多企业将大规模数据分析服务从部署在并行数据库的高端服务器转移至更便宜的无共享体系结构的低端服务器集群上.论文提出了集成MapReduce和数据库的高效经济SaaS架构—HyDB系统,解决海量结构化,半结构化与非结构化数据的高效查询服务,通过对数据的存储模型和查询模型进行研究,提出了完整的数据存储和查询服务方案,给出基于队列的作业调度算法,并支持针对简约数据查询的快速响应模式.最后通过可扩展实验,证明了该系统架构具有良好的加载性能、查询性能和容错能力,可以为用户提供优质的数据服务.     

基于SaaS模式的决策支持系统的研究

SaaS平台软件交付模式主要是把应用软件以服务的形式提供给用户,文中尝试探索了一种基于SaaS模式的决策支持系统的架构。主要方法是采用第三种成熟度模式来设计决策支持系统的架构,对于决策模型主要采用WebService来对服务决策模型进行封装。基于SaaS模式的决策支持系统就可以根据需求来绑定所需要的决策模型,通过实例来阐述模型绑定的关键步骤。分析了用户访问决策模型的具体流程,给出了使用决策模型的形式语言描述。     

一种带租户演化容忍度的SaaS服务演化一致性判定方法

随着云技术的不断发展与成熟,软件即服务(SaaS)模式成为未来软件应用发展的主要趋势。在多元开放的网络生态环境中,SaaS服务若要有效应对用户需求及外部变化,就须具备演化能力。演化一致性 是指服务在演化后能保有原基础及与其他服务正常交互的能力。目前对演化一致性的判定多偏向于定性分析,且往往忽略了租户的感受,没有既定的显式标准对一致性进行定量度量并判定。针对此问题,从SaaS多租户单实例的应用模式出发,分层次细粒度地建立服务实例描述模型,引入一致性度量值来表示定量计算的结果,充分考虑租户的演化要求,提出一种带租户演化容忍度的判定方法,细粒度地判定演化一致性。最后,结合SaaS应用案例,采用所提方法对演化一致性进行分析判定,实际应用的反馈情况验证了该方法的可行性和有效性。     

Flexible service selection with user-specific QoS support in service-oriented architecture

Along with the development of the service-oriented architecture (SOA) and cloud computing, a large number of service providers have created an intense competitive world of business. Consequently, it is becoming increasingly complex to select a service provider for a user as a result of their various economic and social attributes. In this paper, we state the problem of how to find the appropriate services with satisfying the users' multiple QoS requirements. We consider the service's response time, trust degree and monetary cost. And inspired from the mode of Web search engine, such as Yahoo, Google, we propose an innovative service selection algorithm for SOA systems. The algorithm can recommend a number of suitable services based on the user's QoS requirements.Compared with the existing scheduling algorithms, our solution is much more flexible in supporting the multiple objectives and user personalization. We study the scalability of the algorithm with different numbers of jobs, service providers and QoS criteria. And we find that it can capture user's preferences value in less than six times of job submissions.     

SLA-based admission control for a Software-as-a-Service provider in Cloud computing environments

Software as a Service (SaaS) provides access to applications to end users over the Internet without upfront investment in infrastructure and software. To serve their customers, SaaS providers utilise resources of internal data centres or rent resources from a public Infrastructure as a Service (IaaS) provider. In-house hosting can increase administration and maintenance costs whereas renting from an IaaS provider can impact the service quality due to its variable performance. To overcome these limitations, we propose innovative admission control and scheduling algorithms for SaaS providers to effectively utilise public Cloud resources to maximize profit by minimizing cost and improving customer satisfaction level. Furthermore, we conduct an extensive evaluation study to analyse which solution suits best in which scenario to maximize SaaS provider?s profit. Simulation results show that our proposed algorithms provide substantial improvement (up to 40% cost saving) over reference ones across all ranges of variation in QoS parameters.     

云计算环境中SaaS的接入控制和调度策略研究

SaaS是一种基于网络的软件应用模式,是服务提供商将应用软件统一部署在自己的服务器上,用户根据自己的实际需要,通过互联网向服务提供商订购并支付自己所需的服务。在未来,SaaS模式是占主导地位的云服务模型。文中阐述SaaS的基本概念,介绍了SaaS的参考结构以及服务流程,分析概括了不同类型的服务要求的接人控制策略,总结了不同性能要求作业的调度策略,最后结合已有的云计算环境下的SaaS接入控制和调度策略研究成果,展望了未来的研究方向和亟待解决的关键问题。     

The QoS-based MCDM system for SaaS ERP applications with Social Network

Cloud computing delivers almost all of its services including software, user’s data, system resources, processes and their computation over the Internet. Cloud computing consists of three main classes; Software as a Service, Infrastructure as a Service and Platform as a Service. Using Software as a Service (SaaS), users are able to rent application software and databases which they then install onto their computer in the traditional way. In the Enterprise Resource Planning (ERP) system, the system service environment changed so as to allow the application of the SaaS in the cloud computing environment. This change was implemented in order to provide the ERP system service to users in a cheaper, more convenient and efficient form through the Internet as opposed to having to set up their own computer. Recently many SaaS ERP packages are available on the Internet. For this reason, it is very difficult for users to find the SaaS ERP package that would best suit their requirements. The QoS (Quality of Service) model can provide a solution to this problem. However, according to recent research, not only quality attributes’ identification for SaaS ERP, but also a process for finding and recommending software in the cloud computing environment, has proved to be lacking. In this paper, we propose a QoS model for SaaS ERP. The proposed QoS model consists of 6 criteria; Functionality, Reliability, Usability, Efficiency, Maintainability and Business. Using this QoS model, we propose a Multi Criteria Decision Making (MCDM) system that finds the best fit for the SaaS ERP in the cloud computing environment and makes recommendations to users in priority order. In order to organize the quality clusters, we organized an expert group and got their opinion to organize the quality clusters using Social Network Group. Social Networks can be used efficiently to get opinion by various types of expert groups. In order to establish the priority, we used pairwise comparisons to calculate the priority weights of each quality attribute while accounting for their interrelation. Finally, using the quality network model and priority weights, this study evaluated three types of SaaS ERPs. Our results show how to find the most suitable SaaS ERPs according to their correlation with the criteria and to recommend a SaaS ERP package which best suits users’ needs.     

MCORE: a context-sensitive recommendation system for the mobile Web

Abstract: Recommendation systems for the mobile Web have focused on endorsing particular types of content to users. Today, mobile service providers have a more direct recommendation channel, namely the short messaging service. Therefore, mobile service providers should consider both the timing and context of recommendation messages (push messages) that are sent to users. Mobile service providers can learn context-specific user preferences by analysing mobile Web use logs and user responses to push messages. In this paper, we present a context-sensitive recommendation system that can be used to select the optimal context in which to send recommendation messages. We call this system the mobile context recommender system (MCORE). We compared user responses to push messages delivered in and out of suitable contexts as determined by MCORE. The precision of push messages delivered within a suitable context was higher than that of messages delivered outside of one.     

Consumer preference analysis based on text comments and ratings: A multi-attribute decision-making perspective

With the popularity of social media, extracting consumer preferences from online consumer-generated content is of vital importance for product/service providers to develop tailored marketing strategies. However, existing approaches face difficulties analyzing consumer preferences over different attributes of alternatives (restaurants, hotels, etc.), which hinders product/service providers from comprehensively understanding consumer choice decisions. To address this issue, we solve for the consumer preferences over the attributes represented by attribute weights based on consumers’ historical data, including text comments and overall ratings. Specifically, for each comment and a corresponding rating, we first employ sentiment analysis to calculate values of the attributes, and then develop a quadratic programming model to solve for the weights. Based on a stream of a consumer’s text comments and overall ratings, we can correspondingly obtain a stream of weights indexed by the comment time. We then model this stream of weights as hesitant judgments and employ a hesitant multiplicative programming method to solve for the final weights that fit the consumer’s preferences over attributes at the highest satisficing level. In the application of recommendation, our approach not only provides insights into the consumer’s preferences but also has higher prediction power compared with some state-of-the-art methods.