移动云计算中基于协作式博弈模型的资源分配方案

提出一种基于协作式博弈的资源分配方案。首先, 在服务提供商创建长期可用预留资源公用库完毕后, 通过一种访问控制机制为用户提供移动应用服务, 提出一种优化算法实现访问控制的最优决策; 其次, 提出一种协作式博弈模型进行利益分配, 在服务提供商建立合作后, 通过使用资源公用库而获得的运营收入, 在服务提供商之间进行分配博弈。仿真实验结果表明, 该方法是有效的, 能够帮助服务提供商就容量提升制定最优策略, 以实现利益最大化。     

基于随机博弈的机会网络隐私保护机制

机会网络在数据转发过程中,由于需要依靠陌生节点帮助转发信息或者接收来自陌生节点的数据,这种数据转发机制对普通用户来说具有严重的隐私安全隐患。因此,本文针对机会网络中存在恶意节点窃取用户隐私等安全问题,为机会网络中的用户设计出一种最佳防御策略来防止个人隐私泄露。首先,建立机会网络下的系统模型,使用马尔科夫链刻画普通用户的个人信息的变化过程。在系统模型的基础上,采用随机博弈理论对用户与攻击者之间的攻防关系进行建模;然后,提出一种基于极小极大学习算法的防御策略;最后,通过与传统防御算法的对比实验,证明该算法不但具有较快的收敛速度,且在满足一定用户服务质量的前提下,性能始终优于其他防御策略,是机会网络下一种高效的隐私保护安全机制。     

A game theoretic framework for stochastic multipath routing in self-organized MANETs

In this paper we propose a game theoretic framework for stochastic multipath routing in mobile ad hoc networks (MANETs). In a MANET, intelligent and adaptive attackers may try to hijack, jam or intercept data packets traveling from source to destination. In our proposed game, at each stage the source node keeps track of the available multiple paths, the residual bandwidth of the paths and the strategy of the attackers from the information gathered during the previous stage. Based on these observations, the source node selects a path for data communication and switching strategy among the multiple established paths between the source node and the destination node. Accordingly, it selects an optimal routing strategy to send data packets to the destination at each stage of the game. Using minimax-Q learning, the selected routing strategy maximizes the expected sum of per stage discounted payoff, which is the utilization of residual bandwidth between a source–destination pair along with the probability that the path is safe. Performance analysis and numerical results show that our proposed scheme achieves significant performance gains in terms of residual bandwidth utilization, average end-to-end delay, packet delivery ratio, routing overhead and security.     

Dynamic task assignment and resource management in cloud services by using bargaining solution

Cloud computing provides infrastructure, platform and software as services to customers. For the purpose of providing reliable and truthful service, a fair and elastic resource allocation strategy is essential from the standpoint of service customers. In this paper, we propose a game theoretic mechanism for dynamic cloud service management, including task assignment and resource allocation to provide reliable and truthful cloud services. A user utility function is first devised considering the dynamic characteristics of cloud computing. The elementary stepwise system is then applied to efficiently assign tasks to cloud servers. A resource allocation mechanism based on bargaining game solution is also adopted for fair resource allocation in terms of quality of service of requested tasks. Through numerical experiments, it is shown that the proposed mechanism guarantees better system performance than several existing methods. The experimental results show that the mechanism completes the requested tasks earlier with relatively higher utility while providing a significant level of fairness compared with existing ones. The proposed mechanism is expected to support cloud service providers in elastically managing their limited resources in a cloud computing environment in terms of quality of service. Copyright © 2013 John Wiley & Sons, Ltd.     

基于博弈论的多移动机器人聚集任务路径规划

针对多移动机器人聚集任务的路径规划,建立系统多目标优化数学模型,研究基于博弈论的求解方法。将n个机器人视为博弈的n方,建立Nash均衡博弈模型和共谋合作博弈模型,结合进化策略,对多移动机器人的运动协调进行研究。仿真实验结果表明,博弈分析方法可有效解决多机器人的协调路径规划问题。     

Cloud computing system risk estimation and service selection approach based on cloud focus theory

The main cloud computing service providers usually provide cross-regional and services of Crossing Multi-Internet Data Centers that supported with selection strategy of service level agreement risk constraint. But the traditional quality of service (QoS)-aware Web service selection approach cannot ensure the real-time and the reliability of services selection. We proposed a cloud computing system risk assessment method based on cloud theory, and generated the five property clouds by collecting the risk value and four risk indicators from each virtual machine. The cloud backward generator integrated these five clouds into one cloud, according to the weight matrix. So the risk prediction value is transferred to the risk level quantification. Then we tested the Web service selection experiments by using risk assessment level as QoS mainly constraint and comparing with LRU and MAIS methods. The result showed that the success rate and efficiency of risk assessment with cloud focus theory Web services selection approaches are more quickly and efficient.

     

SLA-based operations of massively multiplayer online games in clouds

Successful massively multiplayer online games (MMOGs) have today millions of registered users and hundreds of thousands of active concurrent players. To be able to guarantee quality of service (QoS) to a highly variable number of concurrent users, game operators statically over-provision a large infrastructure capable of sustaining the game peak load, even though a large portion of the resources is unused most of the time. To address this problem, we introduce in this work a new MMOG ecosystem for hosting and provisioning of MMOGs which effectively splits the traditional monolithic MMOG companies into three main service providers: game providers, game operators, and resource providers. Their interaction is regulated through comprehensive service level agreements (SLA) that establish the price, terms of operation, and compensation for service violations. In our model, game operators efficiently provision resources for MMOGs from multiple cloud providers, based on dynamic load forecasts, and ensure proper game operation that maintains the required QoS to all clients under varying resource availability. Game providers manage multiple distributed MMOGs for which they lease services under strict operational SLAs from game operators to satisfy all client requests. These three self-standing, smaller, more agile service providers enable access to the MMOG market for the small and medium enterprises, and to the current commercial cloud providers. We evaluate, through simulations based on real-life MMOG traces and commercial cloud SLAs, the impact of resource availability on the QoS offered to the MMOG clients. We find that our model can mitigate the negative effects of resource failures within four minutes and that MMOG server consolidation can accentuate the negative effects of failures in a resource-scarce environment. We further investigate different methods of ranking MMOG operational offers with either single or multiple (competing) MMOG providers. Our results show that compensations for SLA faults in the offer selection process can lead up to 11–16 % gain in the game providers’ income. We also demonstrate that adequate ranking of offers can lead to MMOG operational cost reductions from 20 up to 60 %.     

车联网中基于攻防博弈的蜜罐防御及传输策略

随着车联网的快速发展,服务提供商通过将5G基站型路侧单元（RSU,road side unit）部署在靠近车辆的位置,能够迅速为车辆用户提供缓存服务。然而,由于恶意攻击者的存在,其通过控制基站获取权限使基站变为恶意基站,达到身份伪造攻击的目的,并以恶意基站的名义发送消息干扰车辆与可信基站之间的通信链路,容易造成严重的行车安全问题。提出了车联网中基于攻防博弈的蜜罐防御及传输策略,通过部署蜜罐基站混淆攻击者,从而降低车联网中身份伪造攻击的风险,提高车联网数据传输的可靠性。将车联网场景中可信基站与恶意基站之间的交互问题建模为攻防博弈模型,在此基础上可信基站与蜜罐基站联合作为防守方来抵御恶意攻击。可信基站和恶意基站作为攻防博弈双方选择各自相应的策略,构建双方效益函数模型,并结合车辆时延反馈机制,防守方与恶意基站动态调整各自策略。通过调整蜜罐基站与车辆的交互性和IP随机化程度,使防守方的整体效益得到有效提升,并利用混合策略纳什均衡理论得出最优解。大量的仿真实验结果表明,所提出的策略能够在恶意攻击者存在的情况下,提高车联网服务的安全传输性能,对比无蜜罐防御方案,防守方期望效益提升了48.9%,数据...     

A coalitional game approach for fractional cooperative caching in content-oriented networks

In content-oriented networks, popular contents are replicated at the intermediate nodes to enhance content delivery performance. Under cooperative caching, the caching nodes collaborate to leverage one another’s cache capability and to reduce the amount of traffic transferring inside the network. This study considers the cooperation among service providers (SPs). The transferable-payoff coalitional game model is applied for analysis. We investigate the stability of the grand coalition and show that the dual-based cost allocation is in the core. A linear program (LP) minimizing the network bandwidth-expense is used for the characteristic function of the game model. However, solving the LP is a challenge because of a large amount of contents in the network. The Dantzig–Wolfe decomposition approach is further applied to decompose the large-scale problem into many subproblems, which can be solved in parallel. The analysis provides not only a deeper insight into the cooperative cache among SPs but also content placement and distribution strategies as a solution to the LP.     

面向5G网络云原生应用资源调度的博弈优化策略

随着5G网络和云原生技术的发展,面向服务的5G云原生核心网应运而生,传统应用正朝着云原生化方向发展.目前云原生服务提供商和云原生应用商数量众多且关系复杂,使得应用在云原生化过程中的资源调度面临新挑战.提出一种5G网络云原生应用资源调度优化策略,将云原生应用商和云原生服务提供商构建为多主多从的Stackelberg博弈模...     

基于QoS预测与约束层次模型的云服务推荐方法研究

如何精确地预测云服务的QoS,灵活地处理用户的QoS约束是实现可靠云服务推荐的关键问题。针对上述问题,提出了一种基于事例推理的云服务QoS动态预测方法,用于预测候选云服务的QoS值,提高云服务质量的可靠性;提出了一种基于约束层次（Constraint Hierarchy,CH）的QoS评价模型,该模型依据QoS约束对用户业务的重要程度,将其分为必须满足的硬约束层和允许被偏离的软约束层,该模型能够有效地解决过约束问题。通过实验验证了提出的云服务推荐方法的可行性和有效性。     

基于Agent的电子市场结伴购买算法

以电子商务市场中买方Agent的结伴购买为背景，研究了自利主体的动态结盟问题。基于博弈论，考虑协商花费为常数情况，若所有Agent都采用“底线”策略，存在唯一的纳什平衡解。最后给出了计算底线值的方法及算法。     

一种基于协作博弈的虚拟网络嵌入策略

针对现有云服务中虚拟网络嵌入方法无法有效处理硬件故障的不足,提出一种基于协作博弈的高可靠性虚拟网络嵌入策略CG-VNE（virtual network embedding strategy based on cooperative game）,其目标是通过使客户们的接受率最大化使云供应方的收入最大,同时将底层路由器或链路故障导致的虚拟网络中断率降到最低．为了回避虚拟网络映射过程的指数级复杂度,CG-VNE将虚拟网络嵌入问题阐述为两个互相交错的协作博弈：第1个博弈处理虚拟节点映射问题,第2个博弈处理虚拟链路的嵌入问题．通过这两种博弈,虚拟博弈方通过合作即可达到纳什平衡,在提升云提供商的收入的同时有效地处理了路由器和链路的物理故障．全面的仿真实验结果表明,在新客户拒绝率、云服务收入及受到物理故障影响的客户率3个方面,相比于目前大多数虚拟网络嵌入算法而言,CG-VNE的性能提升明显．     

Criteria for Selecting Cloud Service Providers: A Delphi Study of Quality-of-Service Attributes

Customers of cloud service providers (CSPs) use different criteria to judge the quality of cloud services. Based on managerial and technical Quality-of-Service (QoS) attributes, these criteria provide information on service quality and the CSP itself. Thus, it is important to identify relevant QoS to assure success of customers. Using a Delphi study, 16 professionals characterized by different cloud service models, company sizes, and industries identified and ranked QoS according to their relative importance. Our results show consensus on QoS. We identify functionality, legal compliance, contract, geolocation of servers, and flexibility as top QoS and observe increasing importance of managerial QoS.     

A bargaining-driven global QoS adjustment approach for optimizing service composition execution path

In service-based workflow area, service composition is emerging as a promising technology providing more flexible integration means across a variety of distributed heterogeneous applications. A critical research issue in service composition is how to achieve an optimized overall end-to-end quality of service (QoS) composition’s requirements by effectively coordinating QoS constraints of individual service. However, few of existing representative works have well considered economic strategies closely related to competitive market scenarios to address overall QoS issues in service composition. In this paper, we propose a novel global QoS adjustment approach by employing a recursive bargaining strategy to gradually remove QoS constraint violations for optimizing service composition execution path. Our approach mainly exploits the hidden marketplace competitive techniques from economics for developing a novel bargaining strategy. Based on this strategy, a user agent can simultaneously negotiate with more than one provider for driving them into competition. As a result, concessions may be made by service providers depending on their own economic equilibrium theories to offer better QoS values than their initial proposals and thus a user can win more profits. Through recursively using bargaining strategy, critical nodes of an initially built optimal service composition execution path by local optimization policy can be continuously updated to minimize QoS constraint violations by reselecting better service providers. Eventually, a near-optimal service composition execution path can be reconstructed meeting user’s overall QoS requirements. Meanwhile, an experiment and evaluation have been conducted for the sake of demonstrating the feasibility and effectiveness of our proposed approach.     

基于联盟构造博弈的认知无线电网络分布式多目标协作感知算法

协作感知技术可提高认知无线电网络中的频谱资源利用率,但网络节点在形成协作感知联盟的同时也不可避免地引入了额外开销,联盟内节点总希望用较少的额外能量开销达到较大的吞吐量期望.为此,文中提出了协作感知系统的多目标非线性优化问题,然后基于联盟博弈理论为该问题构建了一个不可转移支付的联盟构造博弈模型,在其核心的支付函数的设计中,采用线性加权和的方法同时考虑了节点吞吐最期望和能量消耗两个优化目标.基于该函数,提出了一种分布式多目标联盟构造算法DMCF,其核心是根据优超算子所定义的联盟的帕累托顺序,循环地对联盟进行合并和分裂操作.此外,还证明了DMCF的收敛性和最终联盟划分的稳定性.仿真实验的结果表明,DMCF可有效解决提出的多目标优化问题,与一种分布式随机联盟构造算法DRCF相比,DMCF总能使节点消耗较少能量却达到相对较大的吞吐量期望.在不同网络规模下,DMCF可获得的节点平均吞吐量期望可提升约7.5％,而节点平均能量消耗却可降低约70％.     

QoS约束的云经济资源管理模型研究

提供用户满意的、具有QoS约束的云计算应用是云计算面临的一大难题。提出了以商品市场为原型的云计算经济资源管理模型,其通过云用户与供应商的SLA协商,实现应用服务层QoS到资源设备层QoS的映射,最后利用效用函数的管理策略实现资源的优化调度。     

基于代价感知的云任务处理策略

绿色云计算旨在保证服务质量的前提下,最大限度地降低云服务成本．针对这一问题,提出一种兼顾云数据中心高性能和低成本的策略,通过虚拟机动态迁移等技术进行云任务聚合,关闭空闲的节点以最小化能量消耗,同时考虑虚拟机迁移性能,以此保证任务的服务质量．实验结果表明,代价感知的策略可以减少打开节点的个数,兼顾迁移能耗和迁移延迟,在节能的前提下使服务质量得到保证．     

一种云间合作博弈资源提供方法

为了满足云资源消费者的需求,并有效扩展云资源的服务能力,设计基于云间合作博弈的资源联盟形成算法。以联盟总体利益最大化为目标,将多个云资源提供者间的合作行为建模为联盟博弈模型,从而得到最优联盟结构满足用户方的虚拟机实例请求;设计一种联盟的合并与分裂规则,使得最终联盟结构是稳定不变的;设计基于联盟成员贡献能力的标准化估计Banzhaf值法实现联盟总体利益的个体分割。实验结果表明,在不同虚拟机请求类型测试条件下,云联盟形成算法不仅可以确保更高的联盟总体利益,而且使利益分割更加公平,其算法执行效率也更高。     

A routing defense mechanism using evolutionary game theory for Delay Tolerant Networks

Delay Tolerant Networks (DTNs) often suffer from intermittent disruption due to factors such as mobility and energy. Though lots of routing algorithms in DTNs have been proposed in the last few years, the routing security problems have not attracted enough attention. DTNs are still facing the threats from different kinds of routing attacks. In this paper, a general purpose defense mechanism is proposed against various routing attacks on DTNs. The defense mechanism is based on the routing path information acquired from the forwarded messages and the acknowledgment (ACK), and it is suitable for different routing schemes. Evolutionary game theory is applied with the defense mechanism to analyze and facilitate the strategy changes of the nodes in the networks. Simulation results show that the proposed evolutionary game theory based defense scheme can achieve high average delivery ratio, low network overhead and low average transmission delay in various routing attack scenarios. By introducing the game theory, the networks can avoid being attacked and provide normal transmission service. The networks can reach evolutionary strategy stable (ESS) under special conditions after evolution. The initial parameters will affect the convergence speed and the final ESS, but the initial ratio of the nodes choosing different strategies can only affect the game process.