分布式事务处理系统中超时值的一种计算方法

用超时来解决分布死锁问题的难点是难于确定一个合适的超时值。本文首先给出一类基于微机局网的分布式事务处理系统的构造模型,在对其分析的基础上提出了一个超时值的计算方法STEM,用它可在实际环境中以有限次数的统计试验确定超时值的近似最佳取值点。     

.NET框架中事务处理的方法

事务处理在信息处理系统中的应用越来越广泛。文中分别介绍了Miscrosoft．NET框架的自动事务模型和手动事务模型。并给出在各种应用环境中进行事务处理的方法。     

Web服务事务处理的模型化方法研究

Web服务技术的出现为解决跨组织的过程集成问题提供了一种模式。在集成和交互的过程中,Web服务需要事务的支持来保持数据的一致性。传统协议规范已经能够支持Web服务下的事务处理,但仍然存在着问题,比如不同事务处理规范之间兼容性处理、消息格式不匹配、补偿机制、超时和事务范围确立等问题还有待解决。本文介绍了一种Web服务系统的层次设计,并给出了一种Web服务事务处理的模型化方法。这种方法在设计初期就对事务的补偿机制、超时和事务范围这些问题进行考虑和解决,使事务处理过程更加清晰和直接。     

一种基于数组的在线事务处理方法

在线事务处理中,当数据量大到一定程度时,由一般数理方法处理的响应速度是用户不可接受的.文章提出了一种新的基于数组的在线事务处理方法,大量减少读取磁盘数据表的次数,极大地提高了事务处理的响应速度.     

联机事务处理环境下应用软件的容错设计方法

应用软件的可靠性是联机事务处理的关键。本文提出了联机事务处理环境下应用软件的容错设计方法。该方法具有硬件平台简单，与应用相对独立，容错性好等优点。最后，本文详细地介绍了该方法在实现过程中的一些技术问题。     

存储区域网络在联机事务处理下的性能研究

存储区域网络是近年来流行的一种基于块数据访问的存储解决方案,应用于很多领域。联机事务处理也是目前应用很多的业务,将联机事务处理业务运行在存储区域网络中,能够提高业务的质量。如何保证业务能够快速稳定地运行,或一旦出现问题能够在最短的时间内解决问题都是大家关注的事情。这就需要我们研究其性能,进行性能优化,保障系统高质量运行。     

移动计算中基于权值的自适应事务处理模型

在综合分析现有事务模型的基础上，提出了一种根据事务提交所处的网络环境和事务是否访问热点数据，自动选择子事务处理模型的基于权值的自适应移动事务处理模型——AMTMBW。实验证明，新的事务处理模型在移动事务的撤销率，移动事务的执行时间等方面与其他模型相比，其性能都有较大的提高。     

移动事务处理中间件的研究与设计

移动计算环境下,传统的事务处理中间件已不再适用,移动事务所具备的特性应设计支持移动计算环境的移动事务处理中间件.基于FM-Que移动事务模型,采用组件软件模型设计了移动事务处理中间件,此中间件达到两方面的效果:移动环境的复杂特征和变化对应用透明,移动事务应用可以类似常规应用一样进行开发;应用程序可以利用中间件方便的了解上下文环境信息,有效地实现应用相关的自适应.     

网络购物事务处理系统的设计与实现

对网络分布式应用深入理解的基础上,本文构架了一个在因特网上的数据库事务处理模型。并利用通用对象请求处理的CORBA环境和Java编程技术,模拟一个现实因特网电子商务所必需的网络购物系统。     

网络事务处理系统的设计与实现

根据目前事务处理的复杂性以及Internet广泛应用的现状,提出了基于Web事务处理系统的思想。 讨论了实现事务处理系统的相关技术以及系统实现的方法,并提出了实现事务处理系统的安全性的一种措施。     

基于PSO的考虑完整费用的证券组合优化研究

通过分析中国证券市场证券交易不可拆分、不能卖空的特点以及现存的各种交易费用,建立一个考虑完整交易费用的证券投资组合优化模型,同时给出一个应用粒子群算法(PSO)求解的实例。结果证明该证券投资组合优化模型的完整性和有效性,也表明PSO算法可以快速准确地求解证券投资组合优化问题。     

Large scale interoperability and distributed transaction processing

This article integrates an interoperability architecture, the OSCA^TM architecture, and a distributed transaction processing protocol, the X/Open^® Distributed Transaction Processing model, into a unified model of large scale interoperability and distributed transaction processing. Applications supporting different business operations are often deployed in heterogeneous environments in which applications are stand alone islands and operations are fragmented. But in order to have integrated operations, a loosely coupled system of autonomous applications is required often bound together via a distributed transaction processing protocol. This article describes a model for this configuration. It will propose that the span of control of a transaction manager defines the transaction environment for a single application. Any two applications need not conform to the same supplier's transaction environment nor reside in the same environment. Interoperability must be provided among applications, since any one application cannot assume that any other application is under the control of the same transaction manager. Requirements are imposed upon the interactions of applications to support interoperability. The interface between transaction managers must be compatible with these requirements. Other distributed architecture standards must define the requirements for release independence, resource independence, accessibility transparency, location transparency, contract interfaces, and secure environment.     

基于图优化的SLAM后端优化研究与发展

同时定位与地图构建(SLAM)是当前机器人定位导航的研究热点。从优化模型、优化算法及对结果的评估阐述了基于图优化的SLAM后端优化。在建模方面,分析了动态贝叶斯网络建模、因子图建模和马尔可夫图建模;讨论了后端优化的四种方法,即最小二乘优化方法、松弛优化方法、随机梯度下降优化方法和流形优化四种方法。在结果评估方面,阐述了χ2误差和MSE误差对结果的评估方法。最后,结合当前研究热点对SLAM的发展作出了展望。     

一种改进的协同优化算法及其应用

针对协同优化方法收敛困难、优化效率低的问题,提出了一种改进的协同优化算法—ICO算法。通过引入自适应松弛因子将一致性等式约束转化为不等式约束,同时建立混合惩罚函数,将系统级约束优化问题转化为无约束优化问题,ICO算法较好地克服了传统协同优化算法难于收敛的缺点。标准算例实验结果表明,ICO算法能够有效提高优化的稳定性、可靠性和计算效率。优化结果显示了协同优化算法解决海洋供应船的设计优化问题的有效性,为解决更为复杂工程系统的设计优化问题奠定了基础。     

Continuous-time mean variance portfolio with transaction costs: a proximal approach involving time penalization

This paper proposes a new continuous-time optimization solution that enables the computation of the portfolio problem (based on the utility option pricing and the shortfall risk minimization). We first propose a dynamical stock price process, and then, we transform the solution to a continuous-time discrete-state Markov decision processes. The market behavior is characterized by considering arbitrage-free and assessing transaction costs. To solve the problem, we present a proximal optimization approach, which considers time penalization in the transaction costs and the utility. In order to include the restrictions of the market, as well as those that imposed by the continuous-time space, we employ the Lagrange multipliers approach. As a result, we obtain two different equations: one for computing the portfolio strategies and the other for computing the Lagrange multipliers. Each equation in the portfolio is an optimization problem, for which the necessary condition of a maximum/minimum is solved employing the gradient method approach. At each step of the iterative proximal method, the functional increases and finally converges to a final portfolio. We show the convergence of the method. A numerical example showing the effectiveness of the proposed approach is also developed and presented.     

基于收益的软件过程资源调度优化方法

软件项目管理人员须对软件过程中的各种资源进行优化调度,但依靠主观判断和个人经验的资源调度方法具有不稳定性和不可靠性,需要提供客观可靠的软件过程资源调度方法和工具。基于收益的资源调度优化方法通过对软件过程的资源调度进行建模,描述和定义投入资源产生的收益,分析软件过程中活动、资源和收益的各种约束关系,采用基于动态规划的优化算法以较高效率完成资源调度,使资源在软件过程中有效利用。     

地震数据处理和地震动力学仿真平台的优化研究

介绍了高性能计算集群在地震科学领域的运用,深入分析了地震数据处理和地震动力学模拟计算的特点,阐述了地震科学研究对高性能计算平台的性能要求。以中国地震局地质研究所地震数据处理和地震动力学仿真平台为例,介绍了该平台的架构、软件环境、运维和业务应用等方面内容。重点介绍了该系统的优化措施,并从业务应用、用户、管理三个层面出发,探讨了提升集群系统运行效率的办法。     

An exploratory approach for adaptive policymaking by using multi-objective robust optimization

Developing robust policies for complex systems is a profound challenge because of their nonlinear and unpredictable nature. Dealing with these characteristics requires innovative approaches. A possible approach is to design policies that can be adapted over time in response to how the future unfolds. An essential part of adaptive policymaking is specifying under what conditions, and in which way, to adapt the policy. The performance of an adaptive policy is critically dependent on this: if the policy is adapted too late or too early, significant deterioration in policy performance can be incurred. An additional complicating factor is that in almost any policy problem, a multiplicity of divergent and potentially conflicting objectives has to be considered. In this paper we tackle both problems simultaneously through the use of multi-objective robust simulation optimization. Robust optimization helps in specifying appropriate conditions for adapting a policy, by identifying conditions that produce satisfactory results across a large ensemble of scenarios. Multi-objective optimization helps in identifying such conditions for a set of criteria, and providing insights into the tradeoffs between these criteria. Simulation is used for evaluating policy performance. This approach results in the identification of multiple alternative conditions under which to adapt a policy, rather than a single set of conditions. This creates the possibility of an informed policy debate on trade-offs. The approach is illustrated through a case study on designing a robust policy for supporting the transition toward renewable energy systems in the European Union. The results indicate that the proposed approach can be efficiently used for developing policy suggestions and for improving decision support for policymakers. By extension, it is possible to apply this methodology in dynamically complex and deeply uncertain systems such as public health, financial systems, transportation, and housing.     

面向Lustre集群存储的应用日志分析及系统自动优化框架

在科学计算、大数据处理和人工智能等领域,对相关应用负载进行研究,分析负载I/O模式,揭示应用负载变迁规律等,对指导集群存储系统性能优化十分重要。当前应用种类繁多并且应用快速迭代更新,复杂的环境使得对应用负载的特性挖掘充满挑战。针对以上问题,在生产环境中收集了5个Lustre集群存储共计326天的应用日志信息,对应用负载的访问、负载特性进行了深入的探究与分析,并对已有观察进行了验证和补充。通过对应用日志信息横向、纵向和多维度对比分析与信息挖掘,总结了4个发现,并研究相关发现与以往工作的关联性,结合实际生产环境,给出了相应的系统优化策略与切实可行的实施方案,为用户、维护人员、上层应用开发者和多层存储系统设计等人员提供了相关参考与建议。同时,针对实际应用环境复杂、系统优化工作耗时费力等问题,设计并实现了一种系统自动优化框架(SAOF),SAOF可为指定应用负载提供资源预留、带宽限定等功能,初步测试表明,SAOF能根据系统资源与任务负载需求为不同任务提供自动化的QoS保障。     

RPE query processing and optimization techniques for XML databases

An extent join to compute path expressions containing parent-children and ancestor-descendent operations and two path expression optimization rules, path-shortening and path-complementing, are presented in this paper. Path-shortening reduces the number of joins by shortening the path while path-complementing optimizes the path execution by using an equivalent complementary path expression to compute the original one. Experimental results show that the algorithms proposed are more efficient than traditional algorithms.