一种基于市场模型的网络带宽分配方法

价格手段最近被用来控制网络拥塞和管理网络资源的分配,而价格手段的一个重要特点是可以促进资源的合理分配和利用。本文提出一种基于市场模型的网络带宽分配方法,该方法根据用户的宽带需求和网络系统带宽的使用状况,计算出网络带宽的市场价格,并通过用户有的价格参数进行带宽资源的协凋与分配,分析表明,该方法可以达到优化系统效率和提高用户满意度的目标。     

基于非合作竞价博弈的网络资源分配算法的应用研究

本文运用博弈理论对资源分配技术进行了深入研究,提出了能够反映供求关系的基于竞价的网络资源定价机制,并设计了端系统的效用函数,论证了资源分配博弈中Nash均衡点的存在性和唯一性以及Nash均衡解.基于所提资源分配博弈模型,设计了一种网络资源分配算法(RANG).仿真实验表明,RANG算法能够为端系统的资源数量提供参考,规范端系统竞价,从而使得整个资源的分配趋于合理.     

Responsive pricing modeled with Stackelberg game for next-generation networks

In this paper, we study the problem of resource allocation between users in next-generation networks. The starting assumption is that the service provider tries to come up with convincing offers of service level to subscribers, based on demand responsive pricing scheme, while trying to maximize its profit. We developed two algorithms with different usage-based pricing strategies. Both algorithms solve pricing optimization problem using Stackelberg game with service provider acting as a leader and users behaving as followers. We developed the bandwidth management server to perform automatic optimal bandwidth allocation to each user's session and maximize its expected utility while maximizing the overall service provider's revenue. For both algorithms, we also performed the procedure for optimization of the capacity offered to users.     

Bandwidth allocation and admission control scheme based on Nash bargaining solution for WiMAX systems

Development of fair and efficient bandwidth allocation and admission control schemes is one of the key issues in the design of IEEE 802.16 broadband wireless access systems in time division multiple access (TDMA) mode. In this article, the problem of bandwidth allocation and admission control is formulated as a Nash bargaining model. The nash bargaining solution (NBS) derived from the cooperative game is adopted to maximize the spectrum utilization. Analysis and simulation results show that there is a unique Pareto optimal bandwidth allocation solution by using NBS among various flows. Furthermore, maximum utility of the system can also be maintained by using the admission control policy with different number of connections and variable channel qualities. The total throughput of the proposed scheme is close to the maximal one, while significantly improving fairness compared to the existing solutions.     

Fair and Efficient Spectrum Resource Allocation and Admission Control for Multi-user and Multi-relay Cellular Networks

This paper studies the joint relay selection and spectrum allocation problem for multi-user and multi-relay cellular networks, and per-user fairness and system efficiency are both emphasized. First, we propose a new data-frame structure for relaying resource allocation. Considering each relay can support multiple users, a \(K\) -person Nash bargaining game is formulated to distribute the relaying resource among the users in a fair and efficient manner. To solve the Nash bargaining solution (NBS) of the game, an iterative algorithm is developed based on the dual decomposition method. Then, in view of the selection cooperation (SC) rule could help users achieve cooperation diversity with minimum network overhead, the SC rule is applied for the user-relay association which restricts relaying for a user to only one relay. By using the Langrangian relaxation and the Karush–Kuhn–Tucker condition, we prove that the NBS result of the proposed game just complies with the SC rule. Finally, to guarantee the minimum rate requirements of the users, an admission control scheme is proposed and is integrated with the proposed game. By comparing with other resource allocation schemes, the theoretical analysis and the simulation results testify the effectiveness of the proposed game scheme for efficient and fair relaying resource allocation.     

Fair Multiuser Channel Allocation for OFDMA Networks Using Nash Bargaining Solutions and Coalitions

In this paper, a fair scheme to allocate subcarrier, rate, and power for multiuser orthogonal frequency-division multiple-access systems is proposed. The problem is to maximize the overall system rate, under each user's maximal power and minimal rate constraints, while considering the fairness among users. The approach considers a new fairness criterion, which is a generalized proportional fairness based on Nash bargaining solutions and coalitions. First, a two-user algorithm is developed to bargain subcarrier usage between two users. Then a multiuser bargaining algorithm is developed based on optimal coalition pairs among users. The simulation results show that the proposed algorithms not only provide fair resource allocation among users, but also have a comparable overall system rate with the scheme maximizing the total rate without considering fairness. They also have much higher rates than that of the scheme with max-min fairness. Moreover, the proposed iterative fast implementation has the complexity for each iteration of only$O(K^2Nlog_2 N+K^4)$, where$N$is the number of subcarriers and$K$is the number of users.     

Utility Max-Min Flow Control Using Slope-Restricted Utility Functions

We present a network architecture for the distributed utility max-min flow control of elastic and nonelastic flows where utility values of users (rather than data rates of users) are enforced to achieve max-min fairness. The proposed link algorithm converges to utility max-min fair bandwidth allocation in the presence of round-trip delays without using the information of users' utility functions. To show that the proposed algorithm can be stabilized not locally but globally, we found that the use of nonlinear control theory is inevitable. Even though we use a distributed flow-control algorithm, it is shown that any kind of utility function can be used as long as the minimum slopes of the functions are greater than a certain positive value. Though our analysis is limited to the single-bottleneck and homogeneous-delay case, we believe that the proposed algorithm is the first to achieve utility max-min fairness with guaranteed stability in a distributed manner     

A Noncooperative Game-Theoretic Framework for Radio Resource Management in 4G Heterogeneous Wireless Access Networks

Fourth generation (4G) wireless networks will provide high-bandwidth connectivity with quality-of-service (QoS) support to mobile users in a seamless manner. In such a scenario, a mobile user will be able to connect to different wireless access networks such as a wireless metropolitan area network (WMAN), a cellular network, and a wireless local area network (WLAN) simultaneously. We present a game-theoretic framework for radio resource management (that is, bandwidth allocation and admission control) in such a heterogeneous wireless access environment. First, a noncooperative game is used to obtain the bandwidth allocations to a service area from the different access networks available in that service area (on a long-term basis). The Nash equilibrium for this game gives the optimal allocation which maximizes the utilities of all the connections in the network (that is, in all of the service areas). Second, based on the obtained bandwidth allocation, to prioritize vertical and horizontal handoff connections over new connections, a bargaining game is formulated to obtain the capacity reservation thresholds so that the connection-level QoS requirements can be satisfied for the different types of connections (on a long-term basis). Third, we formulate a noncooperative game to obtain the amount of bandwidth allocated to an arriving connection (in a service area) by the different access networks (on a short-term basis). Based on the allocated bandwidth and the capacity reservation thresholds, an admission control is used to limit the number of ongoing connections so that the QoS performances are maintained at the target level for the different types of connections.     

Priority service and max-min fairness

We study a priority service where users are free to choose the priority of their traffic, but are charged accordingly by the network. We assume that each user chooses priorities to maximize its own net benefit, and model the resulting interaction among users as a noncooperative game. We show that there exists an unique equilibrium for this game and that in equilibrium the bandwidth allocation is weighted max-min fair.     

Coalition-based downlink resource allocation for LTE system with divide-and-conquer approach

To take advantage of the multiuser diversity resulted from the variation in channel conditions among the users,it has become an interesting and challenging problem to efficiently allocate the resources such as subcarriers,bits,and power.Most of current research concentrates on solving the resource-allocation problem for all users together in a centralized way,which brings about high computational complexity and makes it impractical for real system.Therefore,a coalitional game framework for downlink multi-user resource allocation in long term evolution(LTE) system is proposed,based on the divide-and-conquer idea.The goal is to maximize the overall system data rate under the constraints of each user’s minimal rate requirement and maximal transmit power of base station while considering the fairness among users.In this framework,a coalitional formation algorithm is proposed to achieve optimal coalition formation and a two-user bargaining algorithm is designed to bargain channel assignment between two users.The total computational complexity is greatly reduced in comparison with conventional methods.The simulation results show that the proposed algorithms acquire a good tradeoff between the overall system throughout and fairness,compared to maximal rate and max-min schemes.     

Resource allocation scheme for orthogonal frequency division multiple access networks based on cooperative game theory

Cooperative game theory can be applied to orthogonal frequency division multiple access (OFDMA) networks for fair resource allocation. In this work, we consider a comprehensive cross‐layer framework including physical and medium access control layer requirements. We apply two cooperative games, nontransferable utility (NTU) game and transferable utility (TU) game, to provide fairness in OFDMA networks. In NTU game, fairness is achieved by defining appropriate objective function, whereas in TU game, fairness is provided by forming the appropriate network structure. For NTU game, we analyze the Nash bargaining solution as a solution of NTU game taking into account channel state information and queue state information. In a TU game, we show that coalition among subcarriers to jointly provide rate requirements leads to better performance in terms of power consumption. The subcarrier's payoff is determined according to the amount of payoff which that subcarrier brings to the coalition by its participation. We show that although NTU and TU games are modeled as rate adaptive and margin adaptive problems, respectively, both solutions provide a fair distribution of resources with minimum fairness index of 0.8. Copyright © 2012 John Wiley & Sons, Ltd.     

Class-based quality of service over air interfaces in 4G mobilenetworks

We present a framework for quality of service provisioning over the air interfaces in future wireless networks, including 3G enhancement and 4G mobile networks. The framework is based on the paradigm of service classes, wherein each class can exhibit a characteristic behavior in terms of resource allocation over the air interface. Using this QoS framework, future wireless network operators can define their own sets of service classes, choose the preferred way of implementing the QoS behavior of these classes, and offer class-based pricing schemes. The user application can choose the service class that best suits its expectations in terms of QoS and cost of access. A class-based bandwidth scheduling scheme is described as a mechanism to implement this QoS framework over CDMA air interfaces. This scheme incorporates the paradigm of service classes, in conjunction with fair resource allocation and air interface congestion resilience, while allocating air interface bandwidth to mobile users     

无线视频通信中带宽分配策略

本文针对无线网络中多用户传输视频时带宽分配问题,提出了一种基于微观经济学中供需平衡的带宽分配策略。该方法充分考虑了运营商的收益,在运营商和用户收益同时最大且供需平衡时,所求得的频谱价格即为运营商的出售价格。同时给出了针对不同用户等级有不同的用户优先级因子,据运营商提出的带宽价格,彼此相互博弈,最终使得自身效用最大。仿真结果表明:本文提出的博弈论方法存在纳什均衡解和均衡单价;用户等级因子能按照用户的不同等级来分配带宽;系统传输质量相比以往方法有所提高。      

User Friendly Preferential Private Recommendation

To provide preferential protection for users while keeping good service utility, a preferential private recommendation framework ( named as PrefER) is proposed. In this framework, a preferential budget allocation scheme is designed and implemented at the system side to provide multilevel protection. And users' preference is utilized at the user side to improve recommendation performance without increasing users' burden. This framework is generic enough to be employed with other schemes. Recommendation accuracy based on the MovieLens dataset by the collaborative filtering schemes and PrefER are compared and analyzed. The experimental results show that PrefER can provide preferential privacy protection with the improvement of recommendation accuracy.     

Fairness and Load Balancing in Wireless LANs Using Association Control

The traffic load of wireless LANs is often unevenly distributed among the access points (APs), which results in unfair bandwidth allocation among users. We argue that the load imbalance and consequent unfair bandwidth allocation can be greatly reduced by intelligent association control. In this paper, we present an efficient solution to determine the user-AP associations for max-min fair bandwidth allocation. We show the strong correlation between fairness and load balancing, which enables us to use load balancing techniques for obtaining optimal max-min fair bandwidth allocation. As this problem is NP-hard, we devise algorithms that achieve constant-factor approximation. In our algorithms, we first compute a fractional association solution, in which users can be associated with multiple APs simultaneously. This solution guarantees the fairest bandwidth allocation in terms of max-min fairness. Then, by utilizing a rounding method, we obtain the integral solution from the fractional solution. We also consider time fairness and present a polynomial-time algorithm for optimal integral solution. We further extend our schemes for the on-line case where users may join and leave dynamically. Our simulations demonstrate that the proposed algorithms achieve close to optimal load balancing (i.e., max-min fairness) and they outperform commonly used heuristics.     

Stackelberg game theoretic pricing algorithm for bandwidth allocation in cooperative access

Cooperative access among user devices by sharing wireless access bandwidth opens a new paradigm in heterogeneous networks.However,how to stimulate cooperative relay nodes forwarding service data for others and allocating corresponding bandwidth to support it are two key issues in the cooperative access.This paper proposes a Stackelberg game based framework which is benefit participants including relay nodes and client nodes.This framework generalizes the pricing based bandwidth allocation algorithm by the Stackelberg game model,which optimizes the profit of the cooperative relay nodes while guaranteeing the bandwidth requirements of client nodes.We transform the profit maximization problem into a convex problem and solve it using the convex optimization method.The simulation results demonstrate that the proposed framework and corresponding algorithms outperform the bidding weight proportional fairness and fixed value bandwidth allocation ones significantly.     

基于合作博弈的多虚拟机实时迁移带宽分配机制

IaaS云计算平台采用虚拟机实时迁移技术进行资源动态调度和管理。在实际应用场景下,需要并行实时迁移多个虚拟机。由于实时迁移算法本身以最大利用带宽的方式进行数据传输,存在着迁移进程间竞争带宽的问题,无法保证带宽全局最优分配,影响整体迁移的性能。提出一种基于合作博弈的多虚拟机实时迁移带宽分配机制,将带宽分配问题建模为一个纳什议价,通过求解纳什议价解得到帕累托最优的带宽分配方案,并在实际的虚拟化平台上进行了实现。实验结果表明,相比标准的并行实时迁移,所提出的带宽分配机制能够公平有效地分配带宽,提高了并行实时迁移的性能。     

改进的认知无线电频谱共享博弈模型

提出了一种改进的认知无线电频谱共享模型.该模型综合考虑了主用户的收益受到所有次用户请求带宽的影响,以及每个次用户的收益受到其他次用户请求带宽的影响;同时,与次用户竞争的主用户能够通过选择最佳带宽定价来获得最大的收益,相互竞争或合作的次用户通过从有限的频谱中改变请求频谱带宽来获得最大收益.对改进模型的静态博弈、动态博弈及其稳定性、帕累托最优等特性的仿真结果证明该模型适应实际情况.     

基于信道定价的无线虚拟网络资源分配策略:匹配/Stackelberg分层博弈

针对分层匹配博弈不能跟踪信道变化以及循环迭代收敛慢等问题,该文提出一种基于信道定价的无线虚拟网络资源分配策略:匹配/Stackelberg分层博弈。分别以基于流带宽的用户满意度、系统带宽及切片功率作为报酬函数建立3级联合优化模型,并采用匹配/Stackelberg分层博弈求解。在博弈下层,定义移动虚拟网络操作者(MVNOs)m—切片n对m_n及其与用户(UEs)的1对1匹配博弈以代替UEs与MVNOs的多对1匹配,对\begin{document}${m_n}$\end{document}定义基于信道平均信息的切片功率价格,加速上、下一致收敛并使UEs适应信道选择最优,证明均衡点存在并给出了低复杂度的分布式拒绝-接收算法;在博弈上层,基于UEs与已匹配关系,形成基础资源提供者(InPs)与的Stackelberg博弈,给出了基于局部信道信息的功率定价和分配策略,使系统效用及频谱效率基于信道最优。最后定义了双层循环稳定条件及过程。仿真表明,该策略在信道跟踪、频谱效率、效用方面均优于随机定价的匹配/Stackelberg分层博弈以及传统分层匹配博弈。     

基于纳什议价的虚拟网多目标映射算法

请求接受率和节能是虚拟网映射过程中两个至关重要的指标,然而当前虚拟网映射问题的研究仅考虑单一指标,忽略了两者之间的关联和制约,导致虚拟网映射整体性能下降。该文提出一种基于纳什议价的虚拟网多目标映射算法(MOVNE-NB):利用博弈论谈判技术,在纳什议价的框架下协商虚拟网映射的合理解;提出公平议价机制,避免参与人自私决策而导致议价失败。实验表明,MOVNE-NB算法不仅能产生一个帕累托有效解,且实现了请求接受率和节能的公平折中。