一种非线性的分布式网格资源调价算法*

针对网格环境下分布式异步动态调价算法存在均衡价格收敛过程缓慢、调价效率较低的缺点,提出了一种基于市场机制的非线性调价算法.该算法结合了当前超额需求和过去超额需求对资源价格变化的影响,较真实地刻画了需求变化后资源价格的波动过程.实验证明,非线性的调价算法明显地提高了价格收敛速度,降低了调价次数.     

基于动态均衡分析的网格资源宏观调价策略

针对现有网格资源调价研究中多采用静态均衡分析而未考虑动态均衡分析和宏观干预调价的问题，提出一种基于动态均衡分析的宏观调价策略。先结合网格特点建立改进的非线性非均衡调价模型，然后对该模型进行动态性和稳定性分析，并根据分析结果提出宏观调价策略。通过实验验证了模型均衡点的存在性和稳定性，以及调价策略的正确性。     

网格计算中一种基于资源特征的调价策略

在基于市场机制的网格系统中,资源调价问题十分复杂。文章讨论了网格资源价格的二重性,局部最优价格和全局最优价格,根据网格资源的特征将资源划分为孤立资源和依赖资源,针对这两类资源的不合理价格提出了相应的调价策略。孤立资源的调价策略强调抑制资源的垄断价格和保护资源的最低价格,依赖资源的调价策略则强调消除资源局部最优价格和全局最优价格之间的矛盾,以获得能促进网格资源供求全局平衡的均衡价格。文章通过实例说明了本调价机制的有效性和实用性。     

云计算环境下分布式文件系统的负载平衡研究

在云计算环境下,文件以块的形式分布在文件系统中。然而系统状态的更新,如节点加入和离开,会导致文件块在系统中的分布不均衡,从而对系统执行的任务产生性能上的影响。针对该分布式文件系统的文件分布不均衡问题,提出了一种完全分布式的负载平衡算法,并采用了仿真实验对所提出的算法与集中式和分布式的负载均衡算法进行了对比。结果表明,该算法在解决了集中式算法的单点瓶颈的基础上只增加了少量的额外开销,其性能明显优于分布式的负载均衡算法。     

基于退火策略的分布式资源负载均衡算法

针对分布式资源导致的访问热点等一系列问题,建立了一个用于分布式资源退火的处理模型.根据该模型,提出了基于退火策略的分布式资源负载均衡算法;该算法通过访问分类、定向扩散等方法提高了系统性能.性能及试验分析表明,该算法能够减少系统内部通信量,抑制资源扩散的抖动现象等.     

一种资源优化的双最小均衡Web集群区分服务调度算法

在一种新的Web集群体系结构的基础上，提出了一种资源优化的双最小均衡区分服务调度算法：首先在前端调度器按资源均衡度将Web请求分配到各后台服务器．然后将Web请求的优先级与资源均衡度两个特征参数结合起来，综合设计后台服务器的Web请求调度顺序，为了评估该算法的性能，进行了大量的模拟实验．在与其他著名调度策略如分离式调度的对比结果显示：双最小均衡调度算法使Web请求的效率提高了11％，同时很好地实现了区分服务．证实了资源优化调度策略具有一定的普遍意义．     

一种高效的分布式反馈流量负载均衡算法

为解决多节点路由器等系统的分布式流量负载均衡问题,提出一种基于反馈的自适应算法。该算法通过会话划分进行负载任务分组,根据各节点的反馈进行接入分配和负载参数调整,实现负载均衡,给出快速端口检测和初始负载均衡等实现该算法的关键技术。性能分析与实验表明,该算法具有较高的负载均衡度和较低的系统开销。     

基于DHT的P2P系统的负载均衡算法

在基于DHT的结构化P2P系统中，DHT的使用以及节点处理能力的不同导致系统中节点的负载不均衡．现有的负载均衡算法存在两个不足：①负载的转移没有考虑节点之间的链路延迟；②算法依赖于系统中固定位置的某些节点．提出了分布式负载均衡算法：每个节点周期性的收集系统局部负载信息，然后选择链路延迟较小的节点进行负载转移．算法依赖于系统中的所有节点，解决了单点失败问题．同时，负载的转移是在链路延迟较小的节点之间进行的．仿真实验表明，①对于各种系统利用率，该算法都可以获得理想的负载均衡效果；②算法可以使负载转移开销减少45％以上．     

可伸缩分布式动态区间映射算法

提出一种支持权重分布数据的可伸缩分布式动态区间映射算法.该算法能够在存储节点发生变化时,根据可用的资源情况立即重新均衡数据对象分布,从所有存储节点中并行迁移数据对象,且迁移的数据对象数目是最少的.在此基础上提出分布式节点地址计算算法,支持计算节点通过视图校正算法自主学习,自动适应新的系统规模,消除了现有的集中式访问性能瓶颈,使系统具有高可伸缩性.     

网格工作流中基于商品市场的服务选择

服务选择是网格工作流的重要环节。针对现有Web服务组合方法对服务质量（QoS）贪婪索取而不利于网格资源有效利用的问题，提出基于商品市场的"按需"网格服务选择方法：根据面向工作流全局的评价模型，以服务使用者需求为约束条件，将服务选择建模为0-1多维背包问题；并引入经济学中的"价格-供需关系"改进了服务选择模型。仿真实验表明：基于商品市场的服务选择均衡了系统负载，有利于网格资源的高效利用。     

On pricing algorithms for batched content delivery systems

Businesses offering video-on-demand (VoD) and downloadable-CD sales are growing in the Internet. Batching of requests coupled with a one-to-many delivery mechanism such as multicast can increase scalability and efficiency. There is very little insight into pricing such services in a manner that utilizes network and system resources efficiently while also maximizing the expectation of revenue. In this paper, we investigate simple, yet effective mechanisms to price content in a batching context. We observe that if customer behavior is well understood and temporally invariant, a fixed pricing scheme can maximize expectation of revenue if there are infinite resources. However, with constrained resources and potentially unknown customer behavior, only a dynamic pricing algorithm can maximize expectation of revenue. We formulate the problem of pricing as a constrained optimization problem and show that maximizing the expectation of revenue can be intractable even when the customer behavior is well known. Since customer behavior is unlikely to be well known in an Internet setting, we develop a model to understand customer behavior online and a pricing algorithm based on this model. Using simulations, we characterize the performance of this algorithm and other simple and deployable pricing schemes under different customer behavior and system load profiles. Based on our work, we propose a pricing scheme that combines the best features of the different pricing schemes and analyze its performance.     

无线数据网络中基于博弈论方法的功率控制

功率控制是第3代移动通信网络中无线资源管理的一项关键技术，传统的功率控制主要考虑移动通信系统中的话音业务，设计了一个新的基于定价的效用函数，采用了博弈论的分析方法，提出了一个适用于无线数据网络的功率控制框架．证明了非合作功率控制博弈中存在惟一的纳什均衡，设计了一个分布的功率控制算法并证明了算法的收敛性．通过数值仿真来验证算法的性能，讨论了各个用户的传输特性和效用函数中定价因子对系统性能的影响，仿真结果表明用户可以用较低的传输功率获得较高的效用，算法具有较好的收敛性能。     

An online valuation-based sealed winner-bid auction game for resource allocation and pricing in clouds

Cloud computing is able to allocate different resources as virtual machines (VMs) to users, who need only pay for the amount of resources used. Two of the challenges in clouds are resource allocation and pricing in such a way to satisfy both cloud providers and users. Existing allocation and pricing mechanisms cannot guarantee increased profits due to various reasons. A better solution to increase the satisfaction of both parties, which is supported by economic theory, is the employment of auction-based allocation and pricing mechanisms. In these mechanisms, cloud resources and services are awarded based on the highest bids, while winners receive the quality of services expected. However, most existing auction-based mechanisms are inefficient and cannot be used in real clouds due to high computational or communication overhead, the bid function’s time complexity, and/or its inaccurate estimates. In the present paper, a lightweight mechanism is introduced which can be utilized in the real-world application of clouds. The currently proposed mechanism is a winner-bid auction game that seals users’ bids by a multi-criteria valuation-based bid function and sends them to the auctioneer. During scheduling, the auctioneer awards VMs exclusively to users with the highest bids. The presented approach is an online auction whose main aim is to increase the profits of the provider and user from different criteria. While determining the Nash equilibrium, the current study specifies the prices to be paid by users in various cases and proves the truthfulness of the proposed method. Finally, the effectiveness of the presented mechanism is examined through extensive experiments on different simulation scenarios and actual workload data.     

Stackelberg-based dynamic incentive pricing algorithm in heterogeneous ubiquitous network

A challenge in the convergence of heterogeneous networks is how to combine the ubiquitous resources and provide the diversified individual services. This paper designs a market model for aggregating reconfiguration in heterogeneous networks based on the tradeoff between resource allocation and consumers’ requirement. To unify the benefits of operators and consumers, a novel Stackelberg-based dynamic incentive pricing algorithm is proposed. The results of the theoretical analysis and simulation demonstrate that the proposed strategy provides incentive for cooperation by means of appropriate resource allocation, and improves the utilization of network resources, thereby effectively realizing the optimization of the whole network performance.     

A simulation‐based algorithm for optimal pricing policy under demand uncertainty

We propose a simulation‐based algorithm for computing the optimal pricing policy for a product under uncertain demand dynamics. We consider a parameterized stochastic differential equation (SDE) model for the uncertain demand dynamics of the product over the planning horizon. In particular, we consider a dynamic model that is an extension of the Bass model. The performance of our algorithm is compared to that of a myopic pricing policy and is shown to give better results. Two significant advantages with our algorithm are as follows: (a) it does not require information on the system model parameters if the SDE system state is known via either a simulation device or real data, and (b) as it works efficiently even for high‐dimensional parameters, it uses the efficient smoothed functional gradient estimator.     

基于经济模型的资源代理

网格计算是为解决大规模资源密集型问题而提出的新一代计算平台，是当前并行和分布处理技术的一个发展方向，而资源管理是计算网格的关键技术之一。对各种各样可利用资源的整合和管理是网格应用的基础，而资源的分布性、动态性、异构性、自治性和需要协调一致性使得网格资源的管理调度成为一个棘手的问题。目前基于市场的经济资源管理和调度算法非常适合计算网格中的资源管理问题，但有调度价格不能更改、负载平衡等问题。文中提出了“网格环境下基于经济模型的资源代理”，依靠多维QoS指导的调度策略和经济模型的启发式调节资源价格，改进和优化计算网格资源的分配。     

Dynamic application placement in the Mobile Cloud Network

To meet the challenges of consistent performance, low communication latency, and a high degree of user mobility, cloud and Telecom infrastructure vendors and operators foresee a Mobile Cloud Network that incorporates public cloud infrastructures with cloud augmented Telecom nodes in forthcoming mobile access networks. A Mobile Cloud Network is composed of distributed cost- and capacity-heterogeneous resources that host applications that in turn are subject to a spatially and quantitatively rapidly changing demand. Such an infrastructure requires a holistic management approach that ensures that the resident applications’ performance requirements are met while sustainably supported by the underlying infrastructure. The contribution of this paper is three-fold. Firstly, this paper contributes with a model that captures the cost- and capacity-heterogeneity of a Mobile Cloud Network infrastructure. The model bridges the Mobile Edge Computing and Distributed Cloud paradigms by modelling multiple tiers of resources across the network and serves not just mobile devices but any client beyond and within the network. A set of resource management challenges is presented based on this model. Secondly, an algorithm that holistically and optimally solves these challenges is proposed. The algorithm is formulated as an application placement method that incorporates aspects of network link capacity, desired user latency and user mobility, as well as data centre resource utilisation and server provisioning costs. Thirdly, to address scalability, a tractable locally optimal algorithm is presented. The evaluation demonstrates that the placement algorithm significantly improves latency, resource utilisation skewness while minimising the operational cost of the system. Additionally, the proposed model and evaluation method demonstrate the viability of dynamic resource management of the Mobile Cloud Network and the need for accommodating rapidly mobile demand in a holistic manner.     

云环境下基于组合双向拍卖的动态资源定价

云环境下的市场交易机制缺乏灵活性,且在某些情况下定价不合理。为此,提出一种基于组合双向拍卖的动态资源定价模型,给出云资源分配与定价算法,用户通过响应时间出价,资源提供商根据负载情况要价。仿真实验结果表明,该算法与固定比例的定价算法相比,能提高18%的用户利益与9%的资源提供商利益。