基于数据局部性的推测式Hadoop任务调度算法研究

针对Hadoop平台现有任务调度算法优化程度不高的问题, 提出了一种基于数据局部性的推测式任务调度算法。该算法通过计算节点上Map和Reduce任务时长比例, 结合不同节点上数据的局部特性, 采用了比现有算法更精确的任务进度探测方式找出快慢节点, 在快节点上启动剩余时间最长的落后任务的备份任务, 用移动计算代替移动数据。在Hadoop环境中进行了实验, 结果表明该算法比现有算法缩短了任务平均运行时间, 加快了任务的执行效率。     

图像Laplace变换在异构多核工程科学计算加速协处理器上的实现

基于自主研发的新颖异构多核工程科学计算加速协处理器(ESCA)体系结构,实现了图像Laplace变换算法。针对ESCA架构特点,采用子字并行计算和访存延迟隐藏等机制,进行了一系列并行算法优化,并在四核ESCA处理器原型上对图像Laplace变换算法的进行了性能评测。实验结果表明,对于计算密集型计算任务,ESCA处理器具有良好的计算加速效果。     

以用户为中心的异构数据集成方法

为了实现用户数据在不同用户应用程序间的共享,将同一用户分布在不同应用程序上的数据按照统一的数据模型组织,并存放在统一的平台上,用异构数据集成技术处理用户数据模型和统一数据模型间的异构问题,使得不同应用平台之间能实现数据共享。     

An Intra-Server Interconnect Fabric for Heterogeneous Computing

With the increasing diversity of application needs and computing units, the server with heterogeneous pro- cessors is more and more widespread. However, conventional SMP/ccNUMA server architecture introduces communication bottleneck between heterogeneous processors and only uses heterogeneous processors as coprocessors, which limits the efficiency and flexibility of using heterogeneous processors. To solve this problem, this paper proposes an intra-server inter- connect fabric that supports both intra-server peer-to-peer interconnection and I/O resource sharing among heterogeneous processors. By connecting processors and I/O devices with the proposed fabric, heterogeneous processors can perform direct communication with each other and run in stand-alone mode with shared intra-server resources. We design the proposed fabric by extending the de-facto system I/O bus protocol PCIe （Peripheral Computer Interconnect Express） and implement it with a single chip cZodiac. By making full use of PCIe＇s original advantages, the interconnection and the I/O sharing mechanism are light weight and efficient. Evaluations that have been carried out on both the FPGA （Field Programmable Gate Array） prototype and the cycle-accurate simulator demonstrate that our design is feasible and scalable. In addition, our design is suitable for not only the heterogeneous server but also the high density server.     

Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Algorithms for numeric data classification have been applied for text classification. Usually the vector space model is used to represent text collections. The characteristics of this representation such as sparsity and high dimensionality sometimes impair the quality of general-purpose classifiers. Networks can be used to represent text collections, avoiding the high sparsity and allowing to model relationships among different objects that compose a text collection. Such network- based representations can improve the quality of the classification results. One of the simplest ways to represent textual collections by a network is through a bipartite heterogeneous network, which is composed of objects that represent the documents connected to objects that represent the terms. Heterogeneous bipartite networks do not require computation of similarities or relations among the objects and can be used to model any type of text collection. Due to the advantages of representing text collections through bipartite heterogeneous networks, in this article we present a text classifier which builds a classification model using the structure of a bipartite heterogeneous network. Such an algorithm, referred to as IMBHN （Inductive Model Based on Bipartite Heterogeneous Network）, induces a classification model assigning weights to objects that represent the terms for each class of the text collection. An empirical evaluation using a large amount of text collections from different domains shows that the proposed IMBHN algorithm produces significantly better results than k-NN, C4.5, SVM, and Naive Bayes algorithms.     

异构数据库数据迁移的研究与实现

数据是业务运转的基础．是企业的宝贵资产。企业在系统升级过程中往往会遇到数据迁移问题,由于新系统与旧系统经常存在数据结构异构的情况．使数据迁移工作难以实现。开发一款异构数据库数据迁移工具,该工具通过框架程序在ODBC（Open Database Connectivity）开发平台以及XML技术的支持下实现数据结构异构的数据库数据迁移。     

能效优先的用户关联与功率控制协同优化机制

在异构蜂窝网络中,针对系统的能效,提出了一种基于效用函数最大化模型的用户关联与功率控制协同优化方案,该方案表示为非线性混合整数问题。为了求得该问题的最优解,设计了一种迭代算法,首先将原问题转换为带参数的多项式形式的问题,在外层循环利用Dinkelbach方法求得最佳的能效因子,然后在内层循环分别求得最佳的用户关联矩阵和传输功率。最终实验结果表明,用户关联与功率控制协同的优化方案在能效和负载平衡方面比固定功率条件下的用户关联策略的性能更优。     

基于元数据的异构环境物联网管理研究

环境物联网(Environmental Internet of Things,EIo T)已经广泛地应用于环境监测,在各个地区部署不同类型的EIo T系统是一个长期演进的过程。为了对分布于各地的大规模异构EIo T系统进行统一、有效的管理,通过分析环境监测的特点,并进一步分析了基于EIo T的环境监测系统的感知层、传输层与应用层的特点,提取出了各个环境物联网系统中多种类型的、可用于系统管理的元数据。基于这些环境物联网相关的元数据,构建了一个环境物联网系统统一管理框架。该管理框架通过实时感知应用层中数据存储层中的数据异常状态,可以屏蔽不同EIo T子系统中感知层与传输层的差异,这在一定程度上解决了异构性所带来的问题,可以对大规模、分布式部署的多个不同类型的EIo T子系统进行有效管理。     

O,O -二甲基硫代磷酰胺异构反应动力学与绿色集成工艺

研究了硫酸二甲酯浓度和反应温度对O,O-二甲基硫代磷酰胺异构化反应的影响,建立了O,O-二甲基硫代磷酰胺异构化反应的动力学模型.拟合得到了O,O-二甲基硫代磷酰胺在硫酸二甲酯催化作用下的反应动力学模型参数.结果表明,主副反应均为二级反应,主反应的活化能为104 kJ/mol,副反应的活化能为111～116 kJ/mol,降低反应温度可减少副反应的发生.提出了采用硫酸二甲酯为催化剂,O,O-二甲基硫代磷酰胺异构合成O,S-二甲基硫代磷酰胺的绿色集成工艺.     

基于SOAP的企业异构信息系统集成

阐述了SOAP通信协议的构成和实现机制以及与相似协议的比较。基于它的跨平台的能力,将其运用于企业的分布式环境中,实现了异构信息系统间的互操作和跨部门的数据处理,提高企业数据处理的效能,同时为企业异构信息系统集成提供了新的方法。