多云环境下基于博弈论的用户行为分析模型

针对多云环境下用户的分布式拒绝服务攻击缺乏有效处理机制的现状,从云服务提供商收益角度出发,提出多云环境下基于博弈论的用户行为分析模型。模型首先基于博弈论构造收益矩阵,之后利用模糊隶属度函数判定用户的行为,并进一步评估非协作和协作场景下云服务提供商的资源消耗和收益。经仿真验证,协作模型能够在减少资源消耗的基础上,有效地降低云服务提供商遭受分布式拒绝服务攻击的风险,相对于非协作场景,可以将单位资源的收益提高3倍以上,具有很强的现实意义。     

云基础设施安全性研究综述

云计算是当今全球关注的热点,有可能引起信息技术新的变革,但同时也带来了新的安全问题。从云计算环境最基础的层次入手,对云基础设施的安全性进行研究,考察云基础设施安全性的研究状况,从全局角度分析云基础设施存在的安全问题,结合云基础设施的安全服务技术框架讨论云基础设施安全性的主要关键技术,旨在为云基础设施乃至整个云计算环境的安全问题的解决建立良好的基础。     

基于Stackelberg领导模式下的云资源定价研究

资源管理是云计算中最重要的一部分。介绍了基于现有云银行模型的云银行生命周期,并提出了合适的定价策略。它根据不同时期云银行的特点得出不同的定价策略。本文的目的是提供一个自动更新机制来定价云资源,从而最大化所有参与者的利益。     

基于服务级别协议的云资源分配

与网格、集群等传统计算模式不同,云计算为用户提供了一种利用远程计算资源的实用商业模型。在不同的客户之间动态分配云资源池以获得最大收入,成为云服务提供商最为关心的问题。云计算中心需要把面向客户的服务层指标转换为面向系统的操作层指标,根据服务级别协议动态管理云计算资源。研究了基于服务级别协议的服务提供商收入最大化问题,借助排队论模型对资源分配问题进行了形式化描述,然后依据定价机制、服务请求到达率、服务率、可用资源等因素给出了资源最优分配方案。实验结果表明,该算法优于相关算法。     

基于云计算的大数据服务资源评价方法

随着大数据服务领域引入云计算技术,需要调动的云服务资源增多且其拓扑结构变得复杂,因此传统基于服务质量(QoS)的加权评价方法无法动态地评价云计算服务资源的有效性和准确性。针对此问题,文中提出了一种基于博弈优化调度的筛选加权评价方法。此方法引入了用户的体验质量(QoE)评价指标,充分考虑了动态调度的业务和时延特性,通过多个指标的博弈,得到加权评价的参数的纳什均衡点。仿真实验结果表明,所提评估方法能够准确地评价云计算服务资源调度的有效性和准确性,并且适合大数据服务业务的拓展。     

Co-op advertising and pricing models in manufacturer–retailer supply chains

Cooperative (co-op) advertising plays a significant role in marketing programs in conventional supply chains and makes up the majority of promotional budgets in many product lines for both manufacturers and retailers. Nevertheless, most studies to date on co-op advertising have only assumed that the market demand is only influenced by the advertising level but not in any way by the retail price. That is why our work is concerned with co-op advertising and pricing strategies in distribution channels consisting of a manufacturer and a retailer. Four different models are discussed which are based on three non-cooperative games (i.e., Nash, Stackelberg retailer and Stackelberg manufacturer) and one cooperative game. We identify optimal co-op advertising and pricing strategies for both firms mostly analytically but we have to resort to numerical simulations in one case. Comparisons are then made about various outcomes, especially the profits, for all cases. This leads to consider more specifically the cooperation case in which profits are the highest for both the retailer and the manufacturer, and how they should share the extra joint profit achieved by moving to cooperation. We solve this bargain problem using the Nash bargaining model.     

Typical Virtual Appliances: An optimized mechanism for virtual appliances provisioning and management

A computing infrastructure requirement in the cloud computing environment can be specified and composed using virtual appliances, which forms the infrastructure-as-a-service (IaaS). Due to the diversity of user requirements, a large number of virtual appliances may be needed. We propose a mechanism called Typical Virtual Appliances (TVAs), an efficient method for providing virtual appliances. In this paper, we present the concept of TVAs and formulate it as an optimization problem with given constraints. With analysis of the software download logs of real web sites, we discover that the number of user requirements follows a quadratic polynomial distribution, and the user requirements are clustered in nature. According to this finding, we develop a clustering-based TVAs generation algorithm, and we show that this algorithm can achieve the optimal result. The clustering algorithm can generate TVAs, which can be transformed to other virtual appliances easily and efficiently. We further design a TVA Management System (TVAMS) to support this mechanism. The simulation results show that our method can meet most of the user requirements efficiently with low storage overhead.     

Cooperative power-aware scheduling in grid computing environments

Energy usage and its associated costs have taken on a new level of significance in recent years. Globally, energy costs that include the cooling of server rooms are now comparable to hardware costs, and these costs are on the increase with the rising cost of energy. As a result, there are efforts worldwide to design more efficient scheduling algorithms. Such scheduling algorithm for grids is further complicated by the fact that the different sites in a grid system are likely to have different ownerships. As such, it is not enough to simply minimize the total energy usage in the grid; instead one needs to simultaneously minimize energy usage between all the different providers in the grid. Apart from the multitude of ownerships of the different sites, a grid differs from traditional high performance computing systems in the heterogeneity of the computing nodes as well as the communication links that connect the different nodes together. In this paper, we propose a cooperative, power-aware game theoretic solution to the job scheduling problem in grids. We discuss our cooperative game model and present the structure of the Nash Bargaining Solution. Our proposed scheduling scheme maintains a specified Quality of Service (QoS) level and minimizes energy usage between all the providers simultaneously; energy usage is kept at a level that is sufficient to maintain the desired QoS level. Further, the proposed algorithm is fair to all users, and has robust performance against inaccuracies in performance prediction information.     

云计算中TSP问题求解服务的定价机制

旅行商问题(TSP)是一个典型的路径优化问题,在城市交通规划、物流运输、通信网络设置等领域都存在类似的问题和应用。但是,TSP问题的求解是NP难的,当问题规模很大时,必须借助大规模并行计算环境,例如云计算平台,以较大的计算开销来获得可行解。以TSP问题为具体实例,研究云计算服务的定价机制。一般情况下,定价机制要满足公平、灵活、动态、自适应。从公平合理角度来看,影响计算服务定价的因素主要有两方面:一是求解问题的难度,包括计算时间复杂性、空间复杂性、输入输出数据规模等;二是求解服务质量,即服务契约,包括可以作为服务等级协定指标的求解精度、响应时间、资源要求等。由此,提出了一种新的云计算中的服务定价机制:CloudPricing。该机制给出了服务定价的一般和具体原则,并给出了相应的定价公式。针对TSP问题求解,进行了具体的定价实例分析,这对云计算中NP难问题求解服务的定价有参考意义。     

Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers

In the past few years, we have witnessed the proliferation of a heterogeneous ecosystem of cloud providers, each one with a different infrastructure offer and pricing policy. We explore this heterogeneity in a novel cloud brokering approach that optimizes placement of virtual infrastructures across multiple clouds and also abstracts the deployment and management of infrastructure components in these clouds. The feasibility of our approach is evaluated in a high throughput computing cluster case study. Experimental results confirm that multi-cloud deployment provides better performance and lower costs compared to the usage of a single cloud only.     

Cloud computing — The business perspective

The evolution of cloud computing over the past few years is potentially one of the major advances in the history of computing. However, if cloud computing is to achieve its potential, there needs to be a clear understanding of the various issues involved, both from the perspectives of the providers and the consumers of the technology. While a lot of research is currently taking place in the technology itself, there is an equally urgent need for understanding the business-related issues surrounding cloud computing. In this article, we identify the strengths, weaknesses, opportunities and threats for the cloud computing industry. We then identify the various issues that will affect the different stakeholders of cloud computing. We also issue a set of recommendations for the practitioners who will provide and manage this technology. For IS researchers, we outline the different areas of research that need attention so that we are in a position to advice the industry in the years to come. Finally, we outline some of the key issues facing governmental agencies who, due to the unique nature of the technology, will have to become intimately involved in the regulation of cloud computing.     

一种基于机制理论的云市场多类型资源分配定价机制

为避免在云服务资源分配中因参与人的自私性而造成的个人效用与社会收益的冲突,在所有参与人都是理性的这一共同知识假定下,针对云市场多类型资源分配问题,首先给出其形式化描述,设计了基于机制理论的分配定价机制,该机制能在复杂用户任务请求下进行资源有效分配,保证个人效用与社会收益最大化。最后证明该机制满足个体理性、预算均衡和激励兼容性质,并给出该机制下寻求任务请求成本最小和效用最大的算法。     

基于回填算法的时间感知的最小区域任务调度算法

在云计算中,任务调度算法的好坏直接影响着云计算系统的性能,因此,一个优秀的云计算调度任务算法不仅能减少云计算数据中心的压力,更快、更好地处理用户大数据量的请求,而且还能使用户获得更好的用户体验。现有回填算法因考虑的指标过于单一,回填性能不佳,导致最终完成时间较长、任务时延较大的问题。为了解决这些问题,提出了基于回填算法的MRA算法;在此基础上,结合任务申请的处理器核心数与任务预计执行时长的关系,对等待任务进行回填作业。在进行回填作业的同时考虑了虚拟机的负载分布,实现了一定的负载均衡。实验结果表明,在任务最大完成时间、任务队列等待时延和虚拟机负载分布上,MRA算法均表现出优异的性能。     

云计算辅助教学在高职教育中的应用研究

“云计算”概念的出现,表明互联网的发展到了一个新的阶段,为教育未来的发展指明了方向。随着信息技术、计算机技术的飞速发展,云计算辅助教学在教学中被逐渐引入,它既可以丰富教学方式,又能够弥补传统教学中知识抽象的不足,还可以提高了学生的学习效率,必将成为提高教学质量的有效手段。     

云计算与云安全

本文主要从云计算的定义、特点以及所面临的问题引入到云安全,讨论了云安全的概念、核心技术以及重点解决的问题。     

An empirical investigation of end-users’ switching toward cloud computing: A two factor theory perspective

With the diffusion of cloud services, they are becoming an alternative to traditional information technology (IT). Despite the importance of cloud services, relatively few studies have been devoted to an empirical examination of the switching behavior from traditional IT to cloud services at the individual level. Therefore, this study attempts to explore the switching factors (i.e. switching enablers and switching inhibitors) and to empirically examine the relationships between those and users’ intention to switch to cloud services, based on the two-factor theoretic perspective. This study uses a longitudinal design to get survey data from undergraduate students at two universities in Korea in the context of IT switching to Google Apps settings. According to our findings, this study found that users’ switching intention to cloud services was not only positively influenced by expected switching benefits whose antecedents are omnipresence of cloud services and collaboration support, but also negatively influenced by expected switching costs whose antecedents are satisfaction with incumbent IT and breath use of inclement IT. The impacts of switching benefits and costs on switching intention were also positively moderated by end users’ personal innovativeness.     

基于证据理论的云计算信任模型研究

信任模型是研究云计算中信任机制的重要问题,其研究结果可应用于云计算数据安全、服务安全,平台安全等研究.为了解决信任的量化和不确定问题,利用证据理论对信任及信任行为进行建模,并认为信任由直接的服务行为结果和间接的第三方推荐组成.提出信任的传递和聚合方式,并将建立的信任模型通过传递方式和聚合方式进行实验;结果显示该信任模型在聚合和信任行为结果纪录情况下,有利于抑制信任的不确定.     

Disruption management for a dominant retailer with constant demand-stimulating service cost

In this paper, we consider coordination model of a one-manufacturer and multi-retailer supply chain with a dominant retailer’s sales promotion opportunity and possible demand disruption. An appropriate contractual scheme can be used to fully coordinate the supply chain even if the demand disruption occurs. In our study, we also analyze how the demand disruption affects the coordination mechanism. When the demand is disrupted, the manufacturer only needs to adjust the maximum variable wholesale price and the subsidy rate under the linear quantity discount scheme. For each case of the demand disruption, we find that the higher the market share of the dominant retailer, the lower its average wholesale price will be. Meanwhile, the higher service cost leads to the higher subsidy rate provided by the manufacturer. The optimal wholesale/retail price, order quantity and subsidy rate can be greatly influenced by the demand disruption. If the disrupted amount of demand is sufficiently small, however, the manufacturer needs to take some special measures to prevent the retailers from deviating the order quantity of the original plan. To demonstrate these findings, we illustrate our propositions by numerical examples.