一种改进的EPON动态带宽分配算法

 在分析了现有带宽分配算法的基础上,结合已有算法的优缺点,提出一种新的区分业务等级的、保证服务质量的动态带宽分配算法。新算法动态确定轮询周期,分配给高优先级业务请求带宽,按权重对中、低优先级业务进行带宽分配,并考虑到了剩余带宽的再分配。经仿真验证：该方案充分保证了高优先级业务的带宽需求,考虑到了低等级业务可能分不到带宽的情况,体现了带宽分配的公平性,全面提高了上行带宽的利用率,提高了系统的服务质量保证。     

Personal recommendation via unequal resource allocation on bipartite networks

In this paper, we present a recommendation algorithm based on the resource-allocation progresses on bipartite networks. In this model, each node is assigned an attraction that is proportional to the power of its degree, where the exponent β is an adjustable parameter that controls the configuration of attractions. In the resource-allocation process, each transmitter distributes its each neighbor a fragment of resource that is proportional to the attraction of the neighbor. Based on a benchmark database, we find that decreasing the attractions that the nodes with higher degrees are assigned can further improve the algorithmic accuracy. More significantly, numerical results show that the optimal configuration of attractions subject to accuracy can also generate more diverse and less popular recommendations.     

基于Q学习和双向ACO算法的云计算任务资源分配模型设计

云计算异构环境中由于计算和存储资源物理分布的不一致性,往往容易导致在应用传统的调度算法进行任务资源分配时存在调度效率低和负载不均衡的问题,为此,设计了一种基于Q学习和双向ACO算法的云计算任务资源分配模型;首先,引入了基于主从结构的调度模型,并综合考虑任务计算完成时间、网络带宽和延迟等因素设计了资源分配目标函数,然后,设计了基于Q学习的云计算资源初始分配方法,将其获得的最优策略对应的Q值初始化网络中节点的Q值,最后,设计一种结合前向蚂蚁和后向蚂蚁的双向ACO算法实现任务资源的最终分配,并对算法进行了定义和描述;在CloudSim环境下进行仿真实验,结果证明文中方法能有效实现云计算异构环境下的任务资源分配,且与其它方法相比,负载均衡离差值平均约为0.071 5,是一种适用于云计算异构环境的有效资源分配方法。      

Hierarchical multi-objective decision systems for general resource allocation problems

We consider optimization methods for hierarchical power-decentralized systems composed of a coordinating central system and plural semi-autonomous local systems in the lower level, each of which possesses a decision making unit. Such a decentralized system where both central and local systems possess their own objective function and decision variables is a multi-objective system. The central system allocates resources so as to optimize its own objective, while the local systems optimize their own objectives using the given resources. The lower level composes a multi-objective programming problem, where local decision makers minimize a vector objective function in cooperation. Thus, the lower level generates a set of noninferior solutions, parametric with respect to the given resources. The central decision maker, then, parametric with respect to the given resources. The central decision maker, then, chooses an optimal resource allocation and the best corresponding noninferior solution from among a set of resource-parametric noninferior solutions. A computational method is obtained based on parametric nonlinear mathematical programming using directional derivatives. This paper is concerned with a combined theory for the multi-objective decision problem and the general resource allocation problem.The authors are indebted to Professor G. Leitmann for his valuable comments and suggestions.     

基于鱼群算法的OFDMA自适应资源分配

针对多用户正交频分多址系统自适应资源分配问题, 提出了一种新的子载波和基于鱼群算法的功率自适应分配算法. 该算法首先对总功率在子载波间均等分布的条件下进行子载波分配,然后引入鱼群算法并根据给出的兼顾用户公平性与系统容量的适应度函数,通过全局搜索实现用户间的功率分配. 仿真结果表明,新算法在保证用户公平性的同时, 还实现了系统总的传输速率最大化. 关键词： 多用户正交频分多址 资源分配 鱼群算法 速率最大化     

异质化带宽分配下的复杂网络数据流负载问题研究

研究了带有连接边传输容量(带宽)约束的复杂网络上如何提升网络数据流负载问题. 在网络连接边带宽资源总量固定的条件下, 提出了一种异质化带宽分配方案. 引入 "受控边" 概念, 通过加入适当比例的 "受控边", 重新分配带宽资源, 并结合具有拥塞感知能力路由策略的数据流量模型, 利用带宽分配调节数据流量走向, 提高了带宽利用效率, 最终使得网络整体的负载能力较带宽匀质化分配时有显著提升. 分别在Barabási-Albert无标度网络和Watts-Strogtz (WS)小世界网络平台上仿真, 发现按照本文的带宽分配方案, WS小世界网络中节点连接边带宽与网络负载有较强的相关性, 节点连接边带宽分配最均衡的时候, 网络负载能力达到最大. 关键词： 异质化带宽分配 负载 介数 受控边     

Allocation Algorithm for Optical OFDM Multimode Fiber Communication System

Abstract

A fast bits allocation algorithm is proposed for adaptively modulated optical orthogonal frequency division multiplexing multimode fiber communication system to decrease the system bit error rate caused by some orthogonal frequency division multiplexing subcarriers being located at the deep nulls in the high-frequency region of multimode fiber. Based on a lookup table scheme, the new algorithm dramatically decreases the computational complexity of the Greedy algorithm. The simulation results show that the new algorithm has the same allocation result as the Greedy algorithm, but the computational complexity is considerably lower. In addition, adaptive bits allocation can effectively reduce the system bit error rate.     

Dynamic allocation of investments promotes cooperation in spatial public goods game

In this paper, we introduce a dynamic allocation investment mechanism into the spatial public goods game. In the model, if a cooperator i$i$ collects more payoffs from one group centered on the player j$j$ in the last round, he will put more investments to the group in the next round. The intensity of investment is controlled by a tunable parameter α$\alpha$. Simulation results show that the emergence of cooperation is remarkably promoted, and a larger α$\alpha$ induces a higher cooperation level. These interesting phenomena are then extensively explained by pairwise investments, average payoffs and evolution snapshots.     

Combining content and relation analysis for recommendation in social tagging systems

Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks.