基于Stackelberg Game模型的网络性能研究

 通过对多局域世界模型(Multi-Local-World Model,MLWM)的分析,抽取出一个基本的拓扑子结构,并将主从博弈的思想引入到网络流量控制中,建立了一个基于网络拓扑的Stackelberg博弈模型.在该模型下,分析了Nash均衡的存在性与唯一性,求出了Nash均衡时最佳的链路价格和流量,给出了网络最佳运行状态的一种定量描述,最后分析了网络拓扑参数对网络最佳运行状态的影响,研究了该模型下网络拓扑变化引起的网络性能的演化特性.     

基于分级编码算法的媒体流调度策略

介绍了当前广泛使用的媒体流调度策略的优点及技术瓶颈,阐述了分级编码算法的特点,并提出一种基于分级编码算法的媒体流调度策略,通过将音频、视频文件分层,重编码,和资源的合理优化,再配合单播与组播的混合发送.达到降低服务器的网络负载率、提高服务器事务处理能力的目的.     

基于Stackelberg博弈的网络虚拟化资源分配方法

在网络虚拟化环境中,基于Stackelberg博弈模型提出了静态博弈算法下网络运营商对频谱的定价策略及静态和动态博弈算法下各虚拟运营商的频谱分配方法,并推导了各运营商收益最大时的纳什均衡点。该方法能同时满足网络运营商和虚拟网络运营商收益最大化的需求。仿真结果验证了算法的有效性及纳什均衡点的存在性。     

异构蜂窝网络中基于Stackelberg博弈的能效优化算法

设备到设备(Device To Device,D2D)通信允许移动终端无需通过基站而进行直接通信。为提高蜂窝系统能效,引入D2D通信共享频谱资源形成异构蜂窝网络。本文将D2D通信的能效优化问题转化为博弈收益最大化问题,并提出了一种基于Stackelberg博弈的分布式功率控制算法。针对系统模型中存在的跨层干扰以及层内干扰,该算法建立了干扰价格系数与D2D对发送功率之间的函数关系,并求解出给定干扰价格系数下D2D对最佳发送功率的闭合表达式。仿真结果表明所提算法能够在最大化基站端蜂窝用户收益的基础上有效提高D2D对的总能效。      

基于Stackelberg博弈的WCDMA网络收益最大化计费的研究

该文研究了在WCDMA网络中如何选择价格来最大化网络收益.没有采用拥塞相关的计费,而是对每个用户有效传输的单位吞吐量收取固定的费用,但每个用户的传输速率是网络拥塞和单位带宽价格的函数,并在此基础上提出了用户净效用函数.利用Stackelberg博弈,建模网络与用户之间的交互,即一方面网络管理者设定价格,以便实现收益最大化,而用户通过自优化效用函数来寻找新的均衡点对此做出响应.本文提供了网络收益与接纳用户数目的定量关系,并研究了网络降低用户传输速率以增大网络容量和拥塞控制的经济动机.     

Ad hoc网络基于信息的调度算法

针对Ad hoc网络提出一种基于信息的调度策略。分组调度时,既考虑队列中剩余信息的多少又考虑原始的信息长度。仿真结果表明,算法改进了整个系统信息端到端的时延性能,并且不影响网络的吞吐量性能,不需要额外的通信开销。     

基于Stackelberg博弈的多任务协作频谱感知算法

随着移动设备的增多,认知无线电技术诞生,而频谱感知是认知无线电技术中的重要一环。本文将群智感知和频谱感知结合,提出了一种基于Stackelberg博弈的多任务协作频谱感知算法。该算法将融合中心(平台)与次用户分别建模为Stackelberg博弈领导者和从属者。在领导者博弈中,平台给次用户发布最优的报酬值以获得最佳的效用;在从属者博弈中,本文着重考虑了剩余能量对次用户的影响,次用户在平台给的报酬下改变感知时间以获得最优的效用。仿真结果表明,该算法可以提高融合中心对频谱的检测概率。      

基于网络编码的无线Mesh网络启发式定向调度算法研究

网络编码技术是近年来网络通信研究领域的一项重大突破,允许网络节点在传统的“存储—转发”基础上参与数据处理,已成为提高网络吞吐量、鲁棒性和可靠性的有效方法。文章结合网络编码机制与定向天线技术,研究无线Mesh网络公平性优化问题。基于跨层设计的思想,文章给出基于网络编码与定向天线的线性规划模型,并进一步提出一种具有定向调度特点的链路调度机信道分配算法。实验仿真结果表明,该算法优于已有SP-omni,COPE,DDSR算法,吞吐量更大,公平性更好。     

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mn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mn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mn,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mn已匹配关系,形成基础资源提供者(InPs)与mn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Stackelberg博弈的无线资源分配算法

4G系统可以随意更改速率控制方案以满足网络应用的需要,这样在传输协议中系统不遵守速率控制规则的行为更使得4G系统能耗大大提高。针对此4G系统接入选择问题,结合单跟随者Stackelberg博弈模型,文章提出了一种使用简单的效用函数和线性代价函数的4G系统无线资源分配算法,理论证明了其收敛的特性,而且通过仿真对基于Stackelberg博弈的4G系统无线资源分配算法的运行结果进行评价,证明了该算法在纳什均衡态的稳定性,取得了4G系统的最优传输效能策略和子博弈完美纳什均衡。     

分段Logistic混沌映射及其性能分析

 分析了具有逐段线性特性的Tent混沌映射和其推广形式:分段Tent混沌映射在计算机有限精度影响下的性能.在此基础上,根据Logistic混沌映射与Tent混沌映射之间具有的拓扑共轭关系,研究了Logistic混沌映射的推广形式:分段Logistic混沌映射,通过实验分析指出用类似于分段Tent混沌映射的方式来定义分段Logistic混沌映射是不可取的.本文构造了一个全新的分段Logistic混沌映射,通过实验对该映射产生的序列的随机性、初值敏感性等性质进行了研究.结果表明,本文定义的分段Logistic混沌映射产生的序列具有良好的随机性和初值敏感性.     

基于遗传算法的云计算任务调度策略

云计算服务中的任务调度问题一直是学者们的研究重点。使用传统遗传算法进行任务调度时,算法的收敛速度慢、进化速率不高,很难达到理想的结果。文中使用基因空间均匀分布策略对遗传算法初始化种群步骤进行了优化,提高了基因的多样性,有效地改善了传统遗传算法进化效率低的问题,提升了收敛速度。     

Network real-time scheduling algorithm based on multi-feature dynamic priority

Real-time task scheduling system structure and task model were proposed aiming at the network real-time scheduling problem.The task degree of urgency was defined by considering the deadline of task,execution time and interval time between works.The task degree of tightness was proposed based on service-level assurance,according to functional importance of different tasks in the real-time task scheduling system.The thrashing limit for avoiding task switching frequently was acquired through dynamic regulation to task priorities by degree of urgency and degree of tightness,which guaranteed the success rate of tasks execution and utilization ratio of client execution.Test simulation results suggest that the multi-feature dynamic priority scheduling strategy improves the success rate of task scheduling and shorten the average response time,which suggests it has obvious superiority compared with BE and EDF scheduling algorithm.     

Data center traffic scheduling strategy based on Fibonacci tree optimization algorithm

To improve traffic scheduling capabilities in network provider data centers,both network structure and network traffic flow were considered at the same time.The analysis prediction and online scheduling mechanism was proposed in data center based on software defined networking (SDN).Aiming at the multi-dimensional,multi-constrained and multi-modal problems of traffic flow scheduling in data centers,the traffic flow scheduling strategy based on Fibonacci tree optimization (FTO) algorithm was proposed.FTO algorithm was embedded into two stages of analysis prediction and online scheduling,took it advantage of global local alternating and multi-model optimization characteristics,the optimal solution and suboptimal solutions of traffic scheduling had been got at one time.The emulator result shows that,the FTO traffic scheduling strategy can schedule traffic in data centers reasonably,which improves the load balancing capability of network providers' data centers effectively.     

基于自相似流量水平分级预测的网络队列调度算法

网络流量的自相似特性会导致网络中数据的突发状态持续,为有效降低网络流量突发引起的队列排队时延和分组丢失率,提高不同优先级业务的传输能力,保障业务服务质量需求,提出了一种基于网络流量自相似特性的队列调度算法——P-DWRR。该算法设计了基于自相似流量水平分级预测结果的动态权值分配方法及服务量子更新方法,并根据业务优先级和队列等待时间确定队列的服务次序,以减小数据分组排队时延,降低分组丢失率。仿真结果表明,P-DWRR算法在满足网络不同业务优先级要求的基础上,降低了数据分组的排队时延、时延抖动和分组丢失率,性能优于DWRR和VDWRR。     

Dual-target WOA spectrum sharing algorithm based on Stackelberg game

In order to solve the complex spectrum allocation problem,a dual-target whale optimization algorithm (WOA) with strong parallel computing capabilities was introduced,and a Stackelberg game model was proposed that could effectively reflect the actual spectrum requirements,and a dual-target WOA optimized distributed antenna system (DAS) spectrum sharing scheduling algorithm was designed.Simulation results show that performance comparison is performed from multiple indicators such as optimal pricing and user benefits.The proposed algorithm has a good spectrum sharing allocation effect,can achieve fair and effective spectrum allocation,and provides an important reference for the future communication network spectrum sharing mode.     

云计算及LLF算法的光纤数据差异化调度策略

现有光纤数据差异化调度策略忽视数据节点的排序,导致构建的调度模型效率较低,影响数据调度速度,为提高光纤数据差异化调度能力,提出基于云计算及LLF算法制定光纤数据差异化调度策略。排列LLF算法下松弛度队列顺序,确保松弛度较大任务能够率先完成,基于LLF算法设计数据调度模型,求出光纤数据调度范围,制定云计算环境下数据差异化调度策略,提升队列排序的处理能力,提高光纤数据调度效率。实验结果可知,该调度策略的数据平均计算时间约为263s,数据平均调度时间为186s,验证了所提方法能够有效提升数据计算及调度效率。     

An on‐demand data broadcasting scheduling algorithm based on dynamic index strategy

On‐demand data broadcasting scheduling is an effective wireless data dissemination technique. Existing scheduling algorithms usually have two problems: (1) with the explosive growth of mobile users and real‐time individual requirements, broadcasting systems present a shortage of scalability, dynamics and timeliness (request drop ratio); (2) with the growth of intelligent and entertained application, energy consumption of mobile client cannot be persistent (tuning time). This paper proposes an effective scheduling algorithm LxRxW. It takes into account the number of lost requests during next item broadcasting time, the number of requests and the waiting time. LxRxW can reduce the request drop ratio. At the same time, the algorithm employs a dynamic index strategy to put forward a dynamic adjusting method on the index cycle length (DAIL) to determine the proper index cycle. Extensive experimental results show that the LxRxW algorithm has better performance than other state‐of‐the‐art scheduling algorithms and can significantly reduce the drop ratio of user requests by 40%–50%. The request drop ratio and accessing time of LxRxW with index increase by 1%–2% than LxRxW algorithm without index, but the tuning time decreases by 70%. The index strategy shows that when the index cycle length is less than 20units, it can significantly reduce the average tuning time but when the index cycle length continues increasing, the average tuning time will increase contrarily. DAIL can dynamically determine the length of index cycle. Moreover, it can reach optimal integrated performance of the request drop ratio, the average accessing time and the average tuning time. Copyright © 2013 John Wiley & Sons, Ltd.