LTE-A中继系统中基于吞吐量和公平性博弈的RRM策略

为解决LTE-A系统中继场景下系统吞吐量和用户公平性的均衡问题,提出用户SINR分散度并以此作为公平性的主要参数,改善小区中心用户和边缘用户的公平性。对小区吞吐量和用户公平性进行博弈建模,基于博弈结果提出资源分配的目标均衡算法。仿真结果表明,提出的目标均衡算法将吞吐量、公平性和博弈论结合起来,在保证系统吞吐量的情况下提高了用户公平性,使系统资源利用更加有效。     

公平性机器学习研究综述

随着机器学习在社会中的广泛使用,带来的歧视问题引发广泛的社会争议,这逐步引起了产业界和学术界对机器学习算法公平性问题的浓厚兴趣。目前对公平性度量和机器学习公平性机制的研究仍然处于初级阶段。本文对公平性机器学习的研究进行了调研,首先从公平性的定义出发,比较了衡量公平性指标的方法,然后调研了公平性数据集,对公平性问题的产生进行了分析,接下来对现有的公平性机器学习算法进行归类和比较,最后总结了当前公平性机器学习研究中存在的问题,并对关键问题和重大挑战进行了讨论。     

Petri网弱公平性和公平性关系的进一步研究

在Petri网中,公平性概念的引入是为了讨论网系统中两个变迁发生之间的相互关系。这种关系可以很好地反映出模拟系统的各个事件在资源竞争中的有无饥饿性问题。文中基于弱公平性和公平性的定义与联系,证明了对于有界Petri网,满足弱公平性就满足公平性;同时证明了在两类无界Petri网中,可由弱公平性推导出公平性。并进一步证明其他类型的无界Petri网是不满足公平性的,即无法从弱公平性直接推导出公平性。     

基于动态博弈描述的电子商务协议公平性*

基于通信顺序进程和动态博弈理论对电子商务系统建模,提出了结构化的电子商务协议的公平性, 并基于该模型给出了公平性的形式化定义, 可以用于电子商务协议公平性的分析。与以往工作相比,主要贡献在于：a)系统模型中的失效模型分别对进程和通信失效建模,增加了通信延迟失效模型,可以分析系统失效时满足的公平性; b)收益平衡从参与者获得利益和损失利益两个角度定义公平性,打破了以往公平性定义只能应用于零和交易系统的局限; c)结构化的公平性分别考虑系统诚实参与者中途自愿退出或者因失效中止协议的公平性和有参与者欺骗下的协议的公平性等。后两点解决了系统满足公平性定义但实际上对参与者不公平的矛盾.     

行为时序逻辑中公平性的研究与完善

基于行为时序逻辑(TLA)的并发系统描述,就是对系统的初始状态、系统行为和行为的公平性进行规约和描述,但TLA中的公平性具有局限性,无法准确地描述某些系统的行为,从而限制了TLA的描述能力。通过研究TLA中公平性的推导过程,分析公平性的概念与定义方法,并以实际例子说明它的局限性。在此基础上,提出以加入两级新的公平性方式对其进行完善。最后,证明了新公平性等级之间的蕴涵关系。完善后的公平性具有更强的描述能力,能够对系统进行更完整的描述与规约。     

AIMD算法在公平性收敛中的应用分析

系统地分析了AIMD(addictive increase multiplicative decrease)算法在拥塞控制过程的公平性收敛中的应用,包括分布式动态资源分配模型、公平性指标函数、应用于公平性收敛的AIMD算法以及公平性收敛动力学分析等问题。     

无线回程网中的公平性模型研究

在分析无线回程网的三个公平性目标的基础上,提出一种新的带权支流最大最小公平性模型,通过对该模型的非线性规划将其推广到带权支流的一般公平性模型,并给出一般模型的分布式算法,实例仿真分析了几种不同公平性模型下系统的吞吐量和公平性,结果表明该带权支流模型一方面较好的兼顾了吞吐量和公平性,另一方面易于推广且可用分布式算法实现.     

FERSF:随机模型检验引导的公平性增强推荐系统框架

在推荐系统的实际应用中,物品流行度偏差会被系统的反馈循环、机器学习训练模型以及一些外界因素所放大,从而导致大量的长尾物品得不到公平的推荐机会。针对反馈循环放大流行度偏差所导致的公平性问题,首次通过随机模型检验的方法进行公平性分析和增强研究。将基于流行度偏差和反馈循环的传统推荐系统框架建模成DTMC模型,并验证其公平性。实验发现随着反馈循环轮数增加,马太效应加剧,公平性明显减弱。然后提出一种随机模型检验引导的公平性增强的推荐系统框架FERSF:在传统的推荐系统框架回路中增加一个动态公平性阈值检测过程,监测其公平性,并对反馈影响因子进行公平性增强调整以减缓流行度偏差对系统的影响。通过实验分析,与传统的推荐系统相比,FERSF的公平性显著提升;与基于效用函数的公平性改进方法相比,FERSF因结合反馈循环的动态特性,从根本上抑制流行度偏差的放大;与其他针对算法的公平性改进相比,FERSF因基于推荐系统框架建模,兼容性强。     

因特网拥塞控制的公平性研究综述

将网络拥塞控制的公平性研究划分为在同质流网络中的公平性和在异质流网络中的公平性两个方面,公平性研究在两类网络中均有重大的意义.依此划分,分别介绍了近年来拥塞控制公平性研究的重要进展.同质流网络中公平性研究主要是围绕解决TCP流的RTT歧视这一问题而展开和深入的;异质流网络中公平性研究主要是围绕保护正当行为流的问题而不断推进的,目前的研究热点是对用户公平的AQM算法.最后对拥塞控制公平性研究领域未来有价值的研究问题给出了预测,并阐述了对这几个问题的理解.     

一个公平有效的电子支付协议

电子现金体制的最主要内容是提供支付双方相同的公平性，而现有方案几乎都不能提供完全的公平性。该文设计了一个新的电子支付协议，能够为支付过程提供完全的公平性。通过分析，该协议具有更好的不可否认性、公平性、保密性、完整性和认证性，提高了运行效率，对于电子商务应用发展有着重要的意义。     

基于多用户QoS的中继系统功率分配算法

研究了非再生中继满足不同用户速率需求的公平性功率分配问题。提出了基于用户期望速率比例公平性功率分配算法,由于源端无法预先接收用户期望速率信息,该算法在实际操作中存在一定的不可操作性。进一步提出了预均分公平性功率分配算法,可弥补基于用户期望速率比例公平性功率分配算法的不足。仿真结果表明,两种算法都实现了保证用户服务质量(QoS)的最优功率分配以最小化用户速率与期望速率差值,节约网络资源的优化目标。     

Priority Service Provisioning and Max–Min Fairness: A Utility-Based Flow Control Approach

In this paper, a novel priority assignment scheme is proposed for priority service networks, in which each link sets its own priority threshold, namely, the lowest priority the link is willing to support for the incoming packets without causing any congestion. Aiming at a reliable transmission, the source then assigns each originated packet the maximum priority value required along its path, because links may otherwise discard the incoming packets which do not meet the corresponding priority requirements. It is shown that if each source sends the traffic at a rate that is reciprocal to the specified highest priority, a bandwidth max–min fairness is achieved in the network. Furthermore, if each source possesses a utility function of the available bandwidth and sends the traffic at a rate so that the associated utility is reciprocal to the highest link priority, a utility max–min fairness is achieved. For general networks without priority services, the resulting flow control strategy can be treated as a unified framework to achieve either bandwidth max–min fairness or utility max–min fairness through link pricing policy. More importantly, the utility function herein is only assumed to be strictly increasing and does not need to satisfy the strictly concave condition, the new algorithms are thus not only suitable for the traditional data applications with elastic traffic, but are also capable of handling real-time applications in the Future Internet.     

Self-stabilizing strong fairness under weak fairness

Fairness assumptions have a great impact on distributed algorithms. They play a major role in determining the time complexity and the correctness of algorithms, since progress or freedom from various types of starvation may not be guaranteed without fairness assumptions. In this paper, we present a stabilizing deterministic algorithm allowing simultaneous execution of actions for strong fairness under weak fairness assumption, in addition, we show that the proposed algorithm yields a high degree of concurrency. We conclude the paper with some remarks on issues such as time optimal implementation of strong fairness and open problems related to fairness     

因特网拥塞控制的稳定性、公平性和可扩展性

基于对偶模型理论,从公平性、稳定性和可扩展性三个方面结合多个发展线索,对因特网拥塞控制研究的发展和近况进行综述,并阐释和辨析了一些重要概念和算法,如网络拥塞控制算法的公平性概念,基于窗口调整和基于速率调整的端节点算法,调度算法与主动队列管理算法,端节点算法、中间节点算法与对偶模型理论中原始算法、对偶算法及原始-对偶算法的对应关系,基于速率和基于队列的中间节点算法。对这一研究领域的最新研究成果作了更全面的分析和更深层次的理解。最后指出了几个有意义的研究方向。     

OFDMA Relay网络二级调度算法性能分析

针对两跳OFDMA Relay网络的特点,将单跳无线网络中3种经典调度算法(轮循(RR)算法、最大载干比(MaxC/I)算法和正比公平(PF)算法)推广到两跳场景中,得到适合两跳中继网的3种二级调度算法E-RR、E-MaxSNR、E-PF。仿真分析表明,3种调度算法的性能比原有算法有较大提升,E-RR能获得好的用户公平性,E-Max C/I可以获得最大的系统吞吐量,E-PF在用户公平性和系统吞吐量间可以取得良好的折中。     

带宽分配中效率与公平性问题研究

本文提出了新的衡量网络带宽分配算法的公平性和效率的定义。基于这个定义对不同的网络带宽公平性分配算法之间的公平性和效率进行了比较。带宽分配问题可以看作是求解一个最大化效用函数的问题,给出了一个新的效用函数,对它在具体网络中的分配和其他公平性分配算法进行比较,建立了它们之间的一种对应关系。     

Price-based congestion control and local channel-link assignment for multi-radio wireless mesh networks

In multi-radio multi-channel wireless mesh networks, engineering the network capacity requires a complex cross-layer design. In this paper, in order to make the complex problem implementable in a distributed manner, we make a decoupling approach that breaks down the entire design space into routing and initial channel assignment, and distributed congestion control and local channel reassignment. We propose a unified priced-based framework for distributed congestion control and localized channel-link assignment algorithms. We demonstrate the convergence of the proposed algorithms with respect to different fairness objectives (i.e., proportional fairness and max–min fairness) via simulation on both grid and random topologies. The proposed algorithms achieve faster convergence with less overhead in the control and forwarding plane than previous multi-path based algorithms.     

Efficient fair algorithms for message communication

A computer network serves distributed applications by communicating messages between their remote ends. Many such applications desire minimal delay for their messages. Beside this efficiency objective, allocation of the network capacity is also subject to the fairness constraint of not shutting off communication for any individual message. Processor Sharing (PS) is a de facto standard of fairness but provides significantly higher average delay than Shortest Remaining Processing Time (SRPT), which is an optimally efficient but unfair algorithm. In this paper, we explore efficient fair algorithms for message communication where fairness means that no message is delivered later than under PS. First, we introduce a slack system to characterize fair algorithms completely and develop efficient fair algorithms called Pessimistic Fair Sojourn Protocol (PFSP), Optimistic Fair Sojourn Protocol (OFSP), and Shortest Fair Sojourn (SFS). Then, we prove that a fair online algorithm does not assure minimal average delay attainable with fairness. Our analysis also reveals lower bounds on worst-case inefficiency of fair algorithms. We conduct extensive simulations for various distributions of message sizes and arrival times. During either temporary overload or steady-state operation, SFS and other newly proposed fair algorithms support SRPT-like efficiency and consistently provide much smaller average delay than PS.     

On-Line Multi-Threaded Paging

In this paper we introduce a generalization of Paging to the case where there are many threads of requests. This models situations in which the requests come from more than one independent source. Hence, apart from deciding how to serve a request, at each stage it is necessary to decide which request to serve among several possibilities. Four different on-line problems arise depending on whether we consider fairness restrictions or not, with finite or infinite input sequences. We study all of them, proving lower and upper bounds for the competitiveness of on-line algorithms. The main competitiveness results presented in this paper state that when no fairness restrictions are imposed it is possible to obtain competitive algorithms for finite and infinite inputs. On the other hand, for the fair case in general there exist no competitive algorithms. In addition, we consider three definitions of competitiveness for infinite inputs. One of them forces algorithms to behave efficiently at every finite stage, while the other two aim at comparing the algorithms' steady-state performances. A priori, the three definitions seem different. We study them and find, however, that they are essentially equivalent. This suggests that the competitiveness results that we obtain reflect the intrinsic difficulty of the problem and are not a consequence of a too strict definition of competitiveness.     

On-Line Multi-Threaded Paging

Abstract. In this paper we introduce a generalization of Paging to the case where there are many threads of requests. This models situations in which the requests come from more than one independent source. Hence, apart from deciding how to serve a request, at each stage it is necessary to decide which request to serve among several possibilities. Four different on-line problems arise depending on whether we consider fairness restrictions or not, with finite or infinite input sequences. We study all of them, proving lower and upper bounds for the competitiveness of on-line algorithms. The main competitiveness results presented in this paper state that when no fairness restrictions are imposed it is possible to obtain competitive algorithms for finite and infinite inputs. On the other hand, for the fair case in general there exist no competitive algorithms. In addition, we consider three definitions of competitiveness for infinite inputs. One of them forces algorithms to behave efficiently at every finite stage, while the other two aim at comparing the algorithms' steady-state performances. A priori, the three definitions seem different. We study them and find, however, that they are essentially equivalent. This suggests that the competitiveness results that we obtain reflect the intrinsic difficulty of the problem and are not a consequence of a too strict definition of competitiveness.