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。     

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.     

A Dantzig-Wolfe Decomposition Based Heuristic Scheme for Bi-level Dynamic Network Design Problem

We present a heuristic to solve the NP-hard bi-level network design problem (NDP). The heuristic is developed based on the Dantzig-Wolfe decomposition principle such that it iteratively solves a master problem and a pricing problem. The master problem is the budget allocation linear program solved by CPLEX to determine the budget allocation and construct a modified cell transmission network for the pricing problem. The pricing problem is the user-optimal dynamic traffic assignment (UODTA) solved by an existing combinatorial algorithm. To facilitate the decomposition principle, we propose a backward connectivity algorithm and complementary slackness procedures to efficiently approximate the required dual variables from the UODTA solution. The dual variables are then employed to augment a new column in the master program in each iteration. The iterative process repeats until a stopping criterion is met. Numerical experiments are conducted on two test networks. Encouraging results demonstrate the applicability of the heuristic scheme on solving large-scale NDP. Though a single destination problem is considered in this paper, the proposed scheme can be extended to solve multi-destination problems as well.     

Integration of pricing with call admission control to meet QoS requirements in cellular networks

Call admission control (CAC) plays a significant role in providing the desired quality of service (QoS) in cellular networks. We investigate the role of pricing as an additional dimension of the call admission control process in order to efficiently and effectively control the use of wireless network resources. First, we prove that, for a given wireless network, there exists a new call arrival rate which can maximize the total utility of users while maintaining the required QoS. Based on this result and observation, we propose an integrated pricing and call admission control scheme where the price is adjusted dynamically based on the current network conditions in order to alleviate the problem of congestion. Our proposed integrated approach implicitly implements a distributed user-based prioritization mechanism by providing negative incentives according to the current network conditions and therefore shaping the aggregate traffic in the network. We compare the performance of our approach in terms of congestion prevention, achievable total user utility, and obtained revenue, with the corresponding results of conventional systems where pricing is not taken into consideration in the call admission control process. These performance results verify the considerable improvement that can be achieved by the integration of pricing in the call admission control process in cellular networks.     

基于效用模型的网络定价策略研究

网络定价问题一直是宽带通信网中的核心问题。在传统定价策略基础上,将服务级别引入效用模型,寻找同时满足客户效用最大化和网络提供商收益最大化的价格均衡点,在理论模型基础上,通过设定具体的用户效用函数以及网路提供商成本函数,运用仿真模拟的方法验证了这种定价策略的现实可行性。     

基于多优先级的网络通信量的价控问题

在多优先级网络中,为了满足各个用户的发送要求,解决网络拥塞并且使用户和网络整体获益,讨论了多优先级网络中通信量的价控问题。使用Nasll平衡和Stackelberg策略,以价格控制为桥梁,调节用户在各优先级的发送量。在假设流量是不同优先级发送信息量函数的情况下,讨论了用户的盈余情况。不仅保证得到较满意的盈余值,而且能使网络更加平衡稳定地运行。改进了对已有的价控问题的研究,从而也可以更加合理、有效地利用网络。仿真结果说明了此方法的适用性。     

A survey of pricing for integrated service networks

Advances in technology have greatly increased the demand for a single integrated service network that can provide multiple service classes for different user requirements. For such a multiple-service network, congestion control is one of the key issues to be addressed. However, without an appropriate mechanism to encourage end users to use the network properly, over-utilization and congestion are unavoidable. For this problem, it is widely accepted that pricing is a proper tool to manage congestion, encourage network growth, and allocate resource to users in a fair manner. However, how to charge for the traffic and at what price is still under study. In this paper, we first briefly review the state of the art and technological growth of congestion control for integrated service networks (ISN). Subsequently, we present a summary of the recent developments on various pricing policies and different charging and billing schemes that have been proposed for ATM and Internet Differentiated Services. Some architecture and implementation issues are also discussed. Finally, some future trends are identified.     

A Tabu search algorithm for the network pricing problem

In this paper, we propose an efficient Tabu Search procedure for solving the NP-hard network pricing problem. By exploiting the problem's features, the algorithm allows the near-optimal solution of problem instances that are out of reach of exact combinatorial methods.     

Utility maximization for communication networks with multipath routing

In this paper, we study utility maximization problems for communication networks where each user (or class) can have multiple alternative paths through the network. This type of multi-path utility maximization problems appear naturally in several resource allocation problems in communication networks, such as the multi-path flow control problem, the optimal quality-of-service (QoS) routing problem, and the optimal network pricing problem. We develop a distributed solution to this problem that is amenable to online implementation. We analyze the convergence of our algorithm in both continuous-time and discrete-time, and with and without measurement noise. These analyses provide us with guidelines on how to choose the parameters of the algorithm to ensure efficient network control.     

Valuation of European continuous-installment options

This paper is concerned with the valuation of European continuous-installment options where the aim is to determine the initial premium given a constant installment payment plan. The distinctive feature of this pricing problem is the determination, along with the initial premium, of an optimal stopping boundary since the option holder has the right to stop making installment payments at any time before maturity. Given that the initial premium function of this option is governed by an inhomogeneous Black-Scholes partial differential equation, we can obtain two alternative characterizations of the European continuous-installment option pricing problem, for which no closed-form solution is available. First, we formulate the pricing problem as a free boundary problem and using the integral representation method, we derive integral expressions for both the initial premium and the optimal stopping boundary. Next, we use the linear complementarity formulation of the pricing problem for determining the initial premium and the early stopping curve implicitly with a finite difference scheme. Finally, the pricing problem is posed as an optimal stopping problem and then implemented by a Monte Carlo approach.     

固定费率下城市出租车拥挤收费模型与算法

针对出租车自身的特殊性, 研究了实施拥挤收费政策过程中出租车是否也应进行拥挤收费。在收费费率已知条件下, 采用组合网络均衡模型描述了出租车拥挤收费前后对出行产生、模式划分、流量分配以及出行分布的影响, 构建了与之等价的最优化模型, 设计了求解模型的启发式算法, 比较了对出租车征收拥挤费前后的社会福利变化。算例表明, 收费前的社会福利总是明显大于收费后的, 并且二者之间的差距随着出行者时间价值(VOT)的增加而减小, 随着弹性灵敏参数的增加而增大。这一研究有助于为拥挤收费政策制定者提供科学的决策依据。     

Online pricing for bandwidth provisioning in multi-class networks

We consider the problem of pricing for bandwidth provisioning over a single link, where users arrive according to a known stochastic traffic model. The network administrator controls the resource allocation by setting a price at every epoch, and each user’s response to the price is governed by a demand function. We formulate this problem as a partially observable Markov decision process (POMDP), and explore two novel pricing schemes––reactive pricing and spot pricing––and compare their performance to appropriately tuned flat pricing. We use a gradient-ascent approach in all the three pricing schemes. We provide methods for computing unbiased estimates of the gradient in an online (incremental) fashion. Our simulation results show that our novel schemes take advantage of the known underlying traffic model and significantly outperform the model-free pricing scheme of flat pricing.     

OFDMA认知无线电网络中面向功率控制的频谱定价与分配

针对OFDMA认知无线电网络,提出一种基于Stackelberg博弈的频谱定价和分配模型.对于次基站控制次网络传输功率来保护主网络通信的场景,主基站可通过该模型获得最优的频谱定价方案.从功率控制的角度,重新设计次用户的效用函数,运用Stackelberg博弈对单个主基站和多个次用户在频谱租赁市场中的交易行为进行建模.通过逆向归纳法,求解市场均衡下的最优频谱定价,使得主基站在考虑主网络QoS降级的同时获得最大收益.此外,对于主基站只能获取本地信息的情形,提出了基于动态Stackelberg博弈的分布式频谱定价和分配模型.仿真实验表明,该模型能够在控制次网络传输功率的基础上,提供最优频谱定价和频谱分配方案.     

基于定价的无线网络拥塞控制策略

随着无线网络规模的扩大以及无线网络设备的急剧增长,网络拥塞问题日益加剧。对此提出了一种基于带宽使用率的动态定价策略,并结合延时奖励机制来解决网络拥塞问题。仿真结果表明,所提出来的控制策略在一定程度上缓解了拥塞的发生。     

基于电子路票征收及补偿的新型离散路网设计与算法

在城市交通网络设计中,可交易电子路票系统是一种新型且更加公平的拥挤收费方法。本文将具有征收与补偿机制的0分配可自由交易电子路票和路网离散改造设计结合起来共同研究城市道路交通网络设计与管理问题。建立的新型路网设计与管理模型同时考虑了0分配电子路票系统与离散路网设计。采用Logit随机用户均衡原理模拟出行者的路线选择行为,并设计具有路段容量和电子路票可行约束的随机均衡问题的有效算法。由于该模型是一个具有不动点约束的数学规划问题,求解比较困难,因此采用常见的群体智能优化算法求解该模型,数值实验验证了模型和算法的有效性和可行性。     

Using heuristic algorithms for capacity leasing and task allocation issues in telecommunication networks under fuzzy quality of service constraints

Nowadays, every firm uses telecommunication networks in different amounts and ways in order to complete their daily operations. In this article, we investigate an optimisation problem that a firm faces when acquiring network capacity from a market in which there exist several network providers offering different pricing and quality of service (QoS) schemes. The QoS level guaranteed by network providers and the minimum quality level of service, which is needed for accomplishing the operations are denoted as fuzzy numbers in order to handle the non-deterministic nature of the telecommunication network environment. Interestingly, the mathematical formulation of the aforementioned problem leads to the special case of a well-known two-dimensional bin packing problem, which is famous for its computational complexity. We propose two different heuristic solution procedures that have the capability of solving the resulting nonlinear mixed integer programming model with fuzzy constraints. In conclusion, the efficiency of each algorithm is tested in several test instances to demonstrate the applicability of the methodology.     

Optimal multi-layered congestion based pricing schemes for enhanced QoS

Pricing is an effective tool to control congestion and achieve quality of service (QoS) provisioning for multiple differentiated levels of service. In this paper, we consider the problem of pricing for congestion control in the case of a network of nodes with multiple queues and multiple grades of service.We present a closed-loop multi-layered pricing scheme and propose an algorithm for finding the optimal state dependent price levels for individual queues, at each node. This is different from most adaptive pricing schemes in the literature that do not obtain a closed-loop state dependent pricing policy. The method that we propose finds optimal price levels that are functions of the queue lengths at individual queues. Further, we also propose a variant of the above scheme that assigns prices to incoming packets at each node according to a weighted average queue length at that node. This is done to reduce frequent price variations and is in the spirit of the random early detection (RED) mechanism used in TCP/IP networks.We observe in our numerical results a considerable improvement in performance using both of our schemes over that of a recently proposed related scheme in terms of both throughput and delay performance. In particular, our first scheme exhibits a throughput improvement in the range of 67–82% among all routes over the above scheme.     

Double-sided auctions applied to vertical handover for mobility management in wireless networks

In the next generation Internet, we have seen the convergence of multimedia services and Internet with the mobility of users. Vertical handover decision (VHD) algorithm are essential components of the mobility management architecture in mobile wireless networks. VHD algorithms help mobile users to choose the best mobile network to connect among available candidates. It also can help the network manager to optimize easily the limited resources shared among the network providers and the users. In this article, we formulate VHD algorithm as a resource allocation problem for down-link transmission power in multiple W-CDMA networks and show how combinatorial double-sided auctions can be applied to this specific problem. The proposed pricing schemes make use of the signal interference to noise ratio, achievable data rates, power allocation at mobile networks, and monetary cost as decision criteria and our model differentiate between new calls and on-going communications to take into account that the last category has somewhat more importance. Several combinatorial double-sided auction are proposed to maximize the social welfare and /or to provide incentives for mobile users and mobile operators to be truth-telling in terms of valuation or cost. Finally, the economic properties of the different proposed pricing schemes are also studied by means of simulations.