网格资源分配的进化博弈策略

针对网格资源分配中的竞争问题,提出了一种利用进化博弈的动态机制研究资源分配的方法。该方法利用复制动态方程求解网格使用者策略选择比例的进化稳定点,通过反复博弈使得网格使用者学习并调整出价策略,并讨论了四种典型的使用者评估函数对进化稳定点的影响。最后利用网格模拟器进行了实验评估,结果表明提出的进化博弈方法是收敛的,且在网格使用者的总体效用方面优于传统算法,从而实现了网格资源的优化分配。     

基于收益率门槛限制考虑的网格资源拍卖问题

基于收益率门槛限制的视角,通过建立效用函数模型并结合动态博弈理论,对网格资源的拍卖问题进行了探讨.在对网格资源提供者与竞标网格资源使用者的动态博弈过程进行分析时发现,网格资源提供者的最优策略选择决定于其对货币收益与非货币收益的偏好程度,以及网格资源使用者的最高报价.在收益率门槛给定的条件下,当参与竞标的网格资源使用者具有较低的生产利润或付出较高的努力成本时,网格资源使用者将会选择价格较低的投标策略.研究结果表明收益率门槛机制的引入,在一定程度上可以使得参与双方的效用达到最大化.     

云计算基于随机进化博弈动态的资源优化配置

云资源优化分配研究中,用户策略难免存在失误,以用户完全理性为前提的资源分配方法在实际环境下会出现失效。分析非完全理性的云用户种群下的资源分配过程及其策略演化过程,提出一种随机动态模型求解有限云种群中的资源分配进化博弈问题。模型克服用户完全理性的限制,利用带有频率依赖选择的Moran过程,在重复博弈过程中寻找云用户策略的入侵指数和固定指数策略选择偏好的条件。根据经济学云环境的特征,建立云用户的固定效用矩阵,量化策略选择动态。数值仿真结果证明,为了最大化自身效用,不同大小云种群中的个体策略会向着不同策略方向演化,资源在有限理性用户之间可以实现优化分配,且最优分配策略最终会处于稳定状态。     

网格资源非对称进化博弈分配策略

针对经济模型的网格系统中资源分配的竞争问题,应用进化博弈论中多种群复制动态博弈模型对有限理性网格用户有差别的出价策略进行了研究,提出了一种非对称进化资源分配博弈模型,该模型将网格用户分为出价偏低的保守种群和出价偏高的激进种群,分析了两种网格种群采取合作与竞争策略的自发进化过程,求解了各自的复制动态方程,并通过实例化的非对称支付矩阵求解了复制动态系统的进化稳定策略。研究表明,只有博弈双方选择对等的行为策略才能促进网格资源的公平分配。     

基于进化博弈的网格资源分配方法的研究

在网络资源优化分配问胚的研究中,由于用户存在决策失误,现有基于理性用户博弈的网格资源分配在实际网格环境会完全失效.分析了非完全理性网格用户群体的资源分配策略及其演化过程,提出一种改进的复制动态机制的网格资源分配方法,克服了用户理性的限制,引入变异机制,在无初始学习样本的情况下,也能确保用户达到进行稳定策略点,实现了网格资源在有限理性用户之间的优化分配.仿真表明,用户通过学习对资源分配策略进行调整,可实现最优分配策略并处于稳定状态,证明了进化博弈的资源分配方法在网格环境中的适应性和稳定性.     

基于博弈理论的经济网格资源配置研究

针对经济网格中,由于网格系统的复杂性和用户的私利性,使得网格用户在资源竞价过程中往往因相关信息的匮乏而导致资源竞价的盲目性问题,根据重复博弈分阶段执行的特点,将网格用户间对网格资源的竞争看作多阶段的重复博弈过程.用户依据前一阶段博弈的竞价值及竞价结果对当前阶段的竞价策略进行调整,通过有限次的阶段博弈达到均衡出价策略组合,实现用户最大效用下的资源分配.仿真表明,在不完全信息的网格环境中,该竞价模型可逐步改善网格用户的资源竞价策略,实现优化目标最大化下的网格资源分配.     

基于非零和随机博弈的APT攻击主动防御策略选取

为解决APT（高级持续性威胁）攻防对抗过程中的防御滞后性问题,并在有限资源下做出最优主动防御决策,针对APT攻击过程中攻防双方意图、可行策略集随攻击阶段推进而演变的特点进行了研究,基于非合作博弈理论构建了多阶段APT攻防随机博弈模型AO-ADSG（APT-oriented attack-defense stochastic game）。针对APT攻防对抗中双方效用不对等的现象引入非零和思想,设计符合APT攻击特征的全资产要素效用量化方法;在分析博弈均衡的基础上给出最优防御策略选取算法。最后,通过“夜龙攻击”模拟实验验证了提出方法的可行性及正确性。     

基于随机博弈与禁忌搜索的网络防御策略选取

网络防御策略是决定网络安全防护效果的关键因素,现有的网络防御决策研究的是完全理性前提条件以及攻防效益函数参数选择等方面,对实际网络攻防中信息不对称、法律惩戒等因素存在模型偏差,降低了策略的实用性与可靠性.结合实际问题,在有限理性的前置条件基础上构建禁忌随机博弈模型,引入了禁忌搜索方法对随机博弈进行有限理性的分析,并设计具有记忆功能的搜索方法,通过禁忌表数据结构实现记忆功能,并利用数据驱动的记忆结合博弈模型得出最优防御策略.实验结果表明：该方法在攻防收益量化方面提高了精准度,防御效益相对于现有典型的方法提高了准确度,方法空间复杂度优于强化学习等典型方法.     

多智能体系统中基于演化博弈的群体状态控制

在对多智能体系统的研究中,如何通过施加最少的控制来使某种策略在群体中占优是一个未解的难题.本文借助演化博弈理论,通过设置一定比例节点为指定策略作为控制手段,分别研究了在无结构群体和随机规则网络群体中的策略演化情况.在随机规则网络中,本文进一步研究了在控制手段下,一种新策略是如何演化并成功占据整个网络的.结果表明在无结构的情况下,强制策略对群体的影响受限于博弈的类型;而在随机规则网络中,在任何的博弈类型下,只要给定足够多的强制策略就可以使其突破成功.在理论分析的基础上,本文进行了计算机仿真验证,仿真结果与理论结果一致.本文的结果揭示了如何对群体施加影响,进而对群体中的个体状态进行控制.     

基于整体效用和区域约束的Agent意图再考虑研究

设计运行在动态环境中的BDI Agent时,为它设计一个合适的意图再考虑策略是一个关键问题。进行了当目标较多时,如何利用目标的分布情况来指导Agent的意图再考虑的研究,提出了一定区域内群体目标的整体效用的概念,提出根据整体效用选择区域并对Agent的行为进行区域约束的方法。在TILEWORLD环境下用实验来考察基于该方法的几种策略,给出了一种较优的意图再考虑的策略。     

An evolutionary game algorithm for grid resource allocation under bounded rationality

Resource allocation cannot reach equilibrium in one‐off game in grid environment because of the bounded rationality of the users. To address this issue, an evolutionary game algorithm for grid resource allocation is proposed in this paper. The evolutionary game theory is introduced to study the selection process of user strategy from the dynamic viewpoint. Firstly, the problem of multiple users competing for a common resource is formulated as a symmetric game. Secondly, replicated dynamic mechanism is used to produce the evolutionary stable point that leads to a satisfied allocation scenario. Finally, the relationships among the evolutionary stable point, valuation functions, and convergence time are discussed in detail. The results of the experiments show that the proposed evolutionary game algorithm is convergent and generates better utility results compared with the classical game algorithm. Copyright © 2009 John Wiley & Sons, Ltd.     

一种基于效用函数的网格资源分配策略

针对网格资源分配中用户需求的异构性问题,提出了一种基于效用函数优化的分配策略。该策略综合考虑用户作业执行费用和执行时间两方面的因素,利用拉格朗日方法解决网格用户效用函数的优化问题,通过二分搜索最优解产生一组优化的用户出价,根据该组出价按比例划分资源的计算能力。该分配策略可对网格资源的价格以及资源的占用时间进行优化,对动态、异构的网格环境具有较好的适　　应性。     

协作式云资源博弈分配

针对用户对云资源的异构性需求和竞争问题,提出了一种协作式资源分配博弈策略。建立了资源分配的协作式博弈模型,定义了协作博弈的用户效用函数和评估函数,证明了在该效用函数下协作博弈存在唯一Nash均衡,并讨论了用户组建联盟对协作特征函数和整体效用的影响。实验结果表明,在该协作博弈策略下,个体用户通过组建联盟集体出价的方式,能够为联盟用户带来更大效用,以收敛方式实现Pareto改进。     

一种基于序贯博弈的网格资源分配策略

网格环境中资源的负载预测是实现资源优化分配的关键任务之一,而网格资源的动态性和异构性使得准确判断资源的负载状态十分困难.针对已有的分配策略对资源负载评估的不足,提出了一种基于序贯博弈的优化用户时间的网格资源分配策略.该策略将正比例资源共享的网格环境中多用户竞争同一计算资源的问题形式化为一个多人序贯博弈,通过寻求该序贯博弈中各个阶段博弈的纳什均衡解来预测资源负载;然后利用此负载信息生成所有用户的最优出价组合和资源的优化价格;最后根据各用户出价,按比例分配资源的计算能力.通过对网格模拟器GridSim的实验研究,结果表明,该策略能够得到合理的用户出价,降低资源占用时间,从而弥补了Bredin提出的优化策略中未考虑资源未来负载变化的缺陷,实现了资源的优化分配.其结论说明运用序贯博弈方法预测资源负载是可行的,且能更好地适应网格环境下异构资源的动态性.     

基于合作博弈的虚拟化资源效用分配策略

效用分配是网格虚拟化资源提供者结成联盟完成用户任务时的关键问题。针对资源提供者建立联盟来提高网格整体效用的情况,研究了利用合作博弈论分配网格资源。给出了资源建立联盟的依据,并以基于费用最小化的MIN_COST算法得到了资源的最优化映射方案。在效用分配中,分别从联盟效用的平均分配和Shapley值分配两方面进行了分析,提出了基于Shapley值的资源联盟效用分配策略。算例结果表明,网格资源联盟可以提高任务的执行效率和资源整体收益,而Shapley值法在均衡联盟个体的效用分配方面也是有效可行的。     

A resource selection scheme for QoS satisfaction and load balancing in ad hoc grid

Ad hoc grids are highly heterogeneous and dynamic, in which the availability of resources and tasks may change at any time. The paper proposes a utility based resource selection scheme for QoS satisfaction and load balancing in ad hoc grid environments. The proposed scheme intends to maximize the QoS satisfaction of ad hoc grid users and support load balancing of grid resources. For each candidate ad hoc grid resource, the scheme obtains values from the computations of utility function for QoS satisfaction and benefit maximization game for ad hoc grid resource preference. The utility function for QoS satisfaction computes the utility value based on the satisfaction of QoS requirements of the grid user request. The benefit maximization game for grid resource node preference computes the preference value from the resource point of view. Its main goal is to achieve load balancing and decrease the number of resource selection failure. The utility value and the preference value of each candidate ad hoc grid resource are combined to select the most suitable grid resource for ad hoc grid user request. In the simulation, the performance evaluation of proposed algorithm for ad hoc grid is conducted.     

Utility-driven solution for optimal resource allocation in computational grid

Optimal resource allocation is a complex undertaking due to large-scale heterogeneity present in computational grid. Traditionally, the decision based on certain cost functions has been used in allocating grid resource as a standard method that does not take resource access cost into consideration. In this paper, the utility function is presented as a promising method for grid resource allocation. To tackle the issue of heterogeneous demand, the user's preference is represented by utility function, which is driven by a user-centric scheme rather than system-centric parameters adopted by cost functions. The goal of each grid user is to maximize its own utility under different constraints. In order to allocate a common resource to multiple bidding users, the optimal solution is achieved by searching the equilibrium point of resource price such that the total demand for a resource exactly equals the total amount available to generate a set of optimal user bids. The experiments run on a Java-based discrete-event grid simulation toolkit called GridSim are made to study characteristics of the utility-driven resource allocation strategy under different constraints. Results show that utility optimization under budget constraint outperforms deadline constraint in terms of time spent, whereas deadline constraint outperforms budget constraint in terms of cost spent. The conclusion indicates that the utility-driven method is a very potential candidate for the optimal resource allocation in computational grid.     

A distributed multiple dimensional QoS constrained resource scheduling optimization policy in computational grid

This paper is to solve efficient QoS based resource scheduling in computational grid. It defines a set of QoS dimensions with utility function for each dimensions, uses a market model for distributed optimization to maximize the global utility. The user specifies its requirement by a utility function. A utility function can be specified for each QoS dimension. In the grid, grid task agent acted as consumer pay for the grid resource and resource providers get profits from task agents. The task agent' utility can then be defined as a weighted sum of single-dimensional QoS utility function. QoS based grid resource scheduling optimization is decomposed to two subproblems: joint optimization of resource user and resource provider in grid market. An iterative multiple QoS scheduling algorithm that is used to perform optimal multiple QoS based resource scheduling. The grid users propose payment for the resource providers, while the resource providers set a price for each resource. The experiments show that optimal QoS based resource scheduling involves less overhead and leads to more efficient resource allocation than no optimal resource allocation.     

Agent based sensors resource allocation in sensor grid

Sensor enabled grid may combine real time data about physical environment with vast computational resources derived from the grid architecture. One of the major challenges of designing a sensor enabled grid is how to efficiently schedule sensor resource to user jobs across the collection of sensor resources. The paper presents an agent based scheme for assigning sensor resources to appropriate sensor grid users on the basis of negotiation results among agents. The proposed model consists of two types of agents: the sensor resource agents that represent the economic interests of the underlying sensor resource providers of the sensor grid and the sensor user agents that represent the interests of grid user application using the grid to achieve goals. Interactions between the two agent types are mediated by means of market mechanisms. We model sensor allocation problems by introducing the sensor utility function. The goal is to find a sensor resource allocation that maximizes the total profit. This paper proposes a distributed optimal sensor resource allocation algorithm. The performance evaluation of proposed algorithm is evaluated and compared with other resource allocation algorithms for sensor grid. The paper also gives the application example of proposed approach.