EDCP——通用于任务复制资源分配算法的复制有效性检查过程

在异构系统中最优化资源分配策略的代价函数是提高分布式系统自治并发处理性能的一个重要途径.基于复制的资源分配策略作为主流策略虽然在异构系统中可以对数据驱动的任务流资源分配上取得很好的效果,但由于大部分的基于复制的资源分配算法都忽略了任务复制本身耗费与其复制所取得的效益之间的平衡比较,因此产生了大量的无效复制,最终影响系统整体性能的提高.针对这种情况,提出了EDCP,可通用于异构系统中基于任务复制的资源分配策略的有效性复制检查过程. EDCP通过对复制的有效性检测,只复制能对系统性能有所提高的任务,减少了通信开销,并且在保证了系统整体效益的前提下最优化了代价函数.     

无线传感器网络中基于延迟与能量平衡的背压调度算法

无线传感器网络作为一种现代化的网络技术已经成熟应用于人们生活的各个领域.但是,如何有效的利用传感节点能量一直是研究无线传感器网络的一个关键问题.背压算法的提出能够保证网络的吞吐量最优化.然而,数据包的延迟性能效果却不是很理想.本文提出了一种基于无线传感器网络的改进的背压算法.在某种程度上,此算法能够确保吞吐量的最优化前提下,有效的综合考虑能量利用与延迟代价相互矛盾的问题.并且,能够在两个因素间得到一个相对平衡的结果.     

面向移动平台的消息通讯服务设计与实现

由于移动平台网络和能耗方面的限制,应用开发者实现并维护一个移动端与服务器端可靠的、低能耗的消息通讯服务代价较高.面向移动平台的消息通讯服务为移动应用提供了即时的、可靠的消息通讯.同时结合移动端上下文信息丰富的特点,消息通讯服务提供了上下文情景感知的消息通讯方式.实验表明了此服务的可行性和低能耗特点.     

非实时连接分布式数据库一致性维护策略

针对非实时连接的分布式数据库系统,考虑其通讯代价和安全性以及实际的需求,提出了一种将数据进行分类,利用弱事务的增量复制来更新数据库,并利用事务优先图来检测冲突的数据库一致性维护策略,它可以有效地节约带宽,减小通信代价。     

基于RSSI的无线传感器网络节点自身定位算法

节点自身定位是无线传感器网络的基础性问题之一.提出了一种基于接收信号强度指示(RSSI)的节点自身定位算法.该算法利用RSSI值估算网络中所有可通信节点间距离的相对大小,得到网络中各节点位置之间的几何约束关系,并以此为约束条件,以锚节点质心和未知节点质心之间的距离最小为目标,将定位问题转化为非线性最优化问题.实验结果显示,当锚节点分布在网络边缘时,该算法可以达到较好的定位效果.     

基于压缩感知的多目标定位与功率估计

因传感器网络中定位问题具有的天然稀疏性,压缩感知理论被广泛应用于其中以减少数据采样量。然而,现有的基于压缩感知的定位技术往往需要目标的发射功率作为先验条件,这并不符合实际中目标完全未知的情况。基于此,提出了一种多目标定位和发射功率估计的方法,该方法将目标位置和功率信息建模成一个稀疏向量,从而将定位和功率估计问题转化为稀疏向量估计问题。该方法包括离线和在线两个阶段:离线阶段主要是部署一些射频发射器并测量接收信号强度值,从而构建感知矩阵;在线阶段中,通过部署少量传感器测量接收信号强度值,求解一个₁范数最优化问题便可精确地重构出稀疏向量。仿真结果验证了该多目标定位和功率估计方法的有效性和鲁棒性。     

网络测量部署模型及其优化算法

ISP(Internet service providers)和企业部署网络监测系统以获取网络的性能数据,确保网络的安全性和连通性,最终加强和改善全局的网络性能.网络监测系统的设计和优化是目前的一个研究热点,其优化目标是最小化监测系统的部署代价和维护代价,并使得对网络的影响尽可能地小.根据测量方式和收集框架的不同,可以设计出不同的网络测量部署模型.这些模型的最优化问题通常是NP难的,一般采用整数规划、设计近似算法和映射到经典优化问题等方法来求取模型的优化解.总结了网络测量部署模型及其优化算法的研究现状,指出了该领域中需要进一步研究的热点问题.     

网格环境中基于对等代理的复制定位机制

针对目前网格环境中复制定位机制存在的问题,并综合考虑其中的优势,提出了一种基于对等代理的分布式复制定位机制,即通过对等代理组成分布式的虚拟全系统,实现全域网络里最大有效范围内的复制定位,比起传统的基于分级机制和分布式查询与索引机制的定位服务,在性能、安全、可靠性和有效性上都有很大的改善和提高。     

无线传感器网络分布式节点定位算法研究

深入分析比较了在无线传感器网络领域中有代表性的4种分布式定位算法,着重关注了算法的能量消耗问题。节点的能量消耗主要由计算和通讯开销组成,对于算法的计算复杂度和通讯开销,做出了定量的分析。在此基础上,对未来的研究与算法的改进提出了建议。     

无线传感器网络节点的加权定位研究

根据实际工程提出一种基于加权定位算法的粮库压力传感器网络模型。运用数学建模、数学分析、最优化理论知识,根据浅圆粮仓粮堆底部力场特性,提出一种加权定位算法,根据此算法构建压力传感器网络模型。对比文献中提出的几种传感器网络模型,该方法优化了传感器节点的布置,提高了监测精度。实验显示该方法具有很高可靠性和适用性。     

The optimal location of replicas in a network using a READ-ONE-WRITE-ALL policy

We consider the problem of locating replicas in a network to minimize communications costs. Under the assumption that the read-one-write-all policy is used to ensure data consistency, an optimization problem is formulated in which the cost function estimates the total communications costs. The paper concentrates on the study of the optimal communications cost as a function of the ratio between the frequency of the read and write operations. The problem is reformulated as a zero-one linear programming problem, and its connection to the p-median problem is explained. The general problem is proved to be NP-complete. For path graphs a dynamic programming algorithm for the problem is presented. Received: May 1993 / Accepted: June 2001     

A linear programming algorithm for optimal portfolio selection with transaction costs

We study the optimal portfolio selection problem with transaction costs. In general, the efficient frontier can be determined by solving a parametric non-quadratic programming problem. In a general setting, the transaction cost is a V-shaped function of difference between the existing and the new portfolio. We show how to transform this problem into a quadratic programming model. Hence a linear programming algorithm is applicable by establishing a linear approximation on the utility function of return and variance.     

Multiple allocation hub-and-spoke network design under hub congestion

The multiple allocation hub-and-spoke network design under hub congestion problem is addressed in this paper. A non-linear mixed integer programming formulation is proposed, modeling the congestion as a convex cost function. A generalized Benders decomposition algorithm has been deployed and has successfully solved standard data set instances up to 81 nodes. The proposed algorithm has also outperformed a commercial leading edge non-linear integer programming package. The main contribution of this work is to establish a compromise between the transportation cost savings induced by the economies of scale exploitation and the costs associated with the congestion effects.     

Mean-variance-skewness model for portfolio selection with transaction costs

A mean-variance-skewness model is proposed for portfolio selection with transaction costs. It is assumed that the transaction cost is a V-shaped function of the difference between the existing portfolio and a new one. The mean-variance-skewness model is a non-smooth programming problem. To overcome the difficulty arising from non-smoothness, the problem was transformed into a linear programming problem. Therefore, this technique can be used to solve large-scale portfolio selection problems. A numerical example is used to illustrate that the method can be efficiently used in practice.     

Optimising the distributed execution of join queries in polynomial time

It is proposed that an optimal strategy for executing a join query in a distributed database system may be computed in a time which is bounded by a polynomial function of the number of relations and the size parameters of the network. The solution so unveiled considers both the transmission costs and the processing costs incurred in delivering the required result to the user that issued the query.The query specifies that several relational tables are to be coalesced and presented to the appropriate user. Undertaking this task demands the utilisation of limited system resources, so that a strategy for fulfilling the request that imposes minimal cost to the system should be devised. Both the processor sites, and the communications links that interconnect them, are utilised; an optimal strategy is one that minimises a weighted sum of processing and data transmission costs.An integer linear programming model of this problem was originally proposed in [1]; however, no suggestion was given as to how this model might be efficiently solved. By extending the earlier analysis, the recursive nature of the join computation is revealed. Further investigations then produce a modified relationship amenable to algorithmic solution; the resultant procedure has polynomial time and space requirements.     

Multiple lot-sizing decisions with an interrupted geometric yield and variable production time

This study examines a multiple lot-sizing problem for a single-stage production system with an interrupted geometric distribution, which is distinguished in involving variable production lead-time. In a finite number of setups, this study determined the optimal lot-size for each period that minimizes total expected cost. The following cost items are considered in optimum lot-sizing decisions: setup cost, variable production cost, inventory holding cost, and shortage cost. A dynamic programming model is formulated in which the duration between current time and due date is a stage variable, and remaining demand and work-in-process status are state variables. This study then presents an algorithm for solving the dynamic programming problem. Additionally, this study examines how total expected costs of optimal lot-sizing decisions vary when parameters are changed. Numerical results show that the optimum lot-size as a function of demand is not always monotonic.     

Optimal sensor scheduling for hidden Markov model state estimation

Consider the Hidden Markov model where the realization of a single Markov chain is observed by a number of noisy sensors. The sensor scheduling problem for the resulting hidden Markov model is as follows: design an optimal algorithm for selecting at each time instant, one of the many sensors to provide the next measurement. Each measurement has an associated measurement cost. The problem is to select an optimal measurement scheduling policy, so as to minimize a cost function of estimation errors and measurement costs. The problem of determining the optimal measurement policy is solved via stochastic dynamic programming. Numerical results are presented.     

考虑电池损耗的电动冷藏车路径及充电策略

电池充电造成的电池损耗对企业运营成本影响较大,以不同SOC区间内一次充电造成的电池容量衰退成本模型估计电池损耗成本,研究了车辆途中可多次进入充电站充电的路径优化问题,在考虑运输成本、制冷成本、货损成本、充电时间成本、惩罚成本的基础上,将电动冷藏车的电池损耗成本纳入总成本最小的目标函数,并建立了线性规划数学模型。采用增加粒子间共享信息类型的改进粒子群算法对该模型进行求解。将改进粒子群算法应用于构造的算例中,得到包括充电策略在内的车辆最优路径方案和最小运营成本,结果表明充电上限为80%的车辆路径方案可得到最低的运营成本,同时与标准粒子群算法求得的计算结果进行了比较分析,证明该改进粒子群算法在求解该问题上的可行性。     

复杂因素下的多用户共享储能系统优化

储能系统(ESS)具有削峰填谷,降低用户用电成本的作用.针对单用户独立投入储能系统成本过大的问题,提出了一种共享模式下的储能系统调度策略.考虑了一次性投资成本、运行维护、寿命周期、削峰获利等因素,以最小化日均投资运行成本为目标函数,加入了储能系统能量约束、功率约束、日均投资维护成本模型等,建立了混合整数线性规划(MILP)模型.研究了不同放电深度对日均投资成本的影响.仿真结果表明共享储能模式相对于单用户储能模式节省14.17%.     

An integrated replenishment model with quantity discounts, reentry and downward substitution for control wafers

This paper considers control wafers replenishment problem in wafer fabrication factories. A dynamic lot-sizing replenishment problem with reentry and downward substitution is examined in a pulling control production environment. The objective is to set the inventory level so as to minimize the total cost of control wafers, where the costs include order cost, purchase cost, setup cost, production cost and holding cost, while maintaining the same level of production throughput. In addition, purchase quantity discounts and precise inventory level are considered in the replenishment model. The control wafers replenishment problem is first constructed as a network, and is then transformed into a mixed integer programming model. Lastly, an efficient heuristic algorithm is proposed for solving large-scale problems. A numerical example is given to illustrate the practicality for empirical investigation. The results demonstrate that the proposed mixed integer programming model and the heuristic algorithm are effective tools for determining the inventory level of control wafers for multi-grades in multi-periods.