基于分布式计算的电网运行可靠性评估算法研究

针对电网运行可靠性评估计算量大,但又要求计算时间短的问题,提出了一种基于分布式计算的电网运行可靠性评估算法。该算法采用改进的状态选择和分析方案,解决了计算终端间随机数序列的关联性问题,有效地降低了通信量,提高了计算速度。基于该算法编制的评估软件已通过RTS-24系统和某省级电网的实际运行测试,测试结果表明,该算法不仅能够有效评估电网运行可靠性,而且计算速度随计算终端数增加呈一定倍数提高。     

实时分布式系统性能测试技术研究

实时分布式系统的性能是关注核心,对其进行评估和测试关系着系统的应用前景。基于实时分布式系统的性能测试需求,对系统关键的处理性能、存储性能、传输性能以及实时恢复性能这四种性能测试方法进行了研究和探讨,同时分别利用实时分布式系统中e600架构的处理器、flash存储器、FC网络以及分区调度策略针对这些方法开展了测试,并对获得的测试结果进行了分析。分析结果表明,性能测试结果可信,方法可行,可作为实时分布式系统评测依据。     

小说人物的分布表示及其应用研究

小说是以刻画人物为中心,通过完整的故事情节和具体的环境描写反映社会生活的一种文学体裁。对小说人物进行建模,是小说文本理解和小说文本挖掘的基础性工作。该文构建了大规模的小说语料库,抽取人物及其依存特征,提出基于skip-gram的人物向量训练方法,以人物为目标,以依存特征为上下文,基于训练出的人物向量,探索了小说人物相似度计算、小说人物聚类分析及小说人物画像等应用。实验结果表明,小说人物的分布表示有较好的应用效果。     

P2P网络中基于RDF/S的数据库语义查询系统设计*

提出一种P2P网络环境下基于RDF/S 数据模型的数据库语义查询原型系统,可以有效地对各种基于ODBC的异构数据源进行语义查询。首先数据内容经RDF/S描述成RDF/S schema片断形式,然后对片段进行编码,再把编码杂凑到DHT中,就可以使用Chord协议定位目标节点。     

论焊接结构生产的技术改造

本文讨论了焊接结构生产技术改造的有关问题，包括总思路及总目标、自动化的基本框架、按低成本自动化的原则设计生产线和关键技术问题的论述。     

Implementation and performance evaluation of a scheduling algorithm for divisible load parallel applications in a cloud computing environment

Cloud computing is an emerging technology in which information technology resources are virtualized to users in a set of computing resources on a pay‐per‐use basis. It is seen as an effective infrastructure for high performance applications. Divisible load applications occur in many scientific and engineering applications. However, dividing an application and deploying it in a cloud computing environment face challenges to obtain an optimal performance due to the overheads introduced by the cloud virtualization and the supporting cloud middleware. Therefore, we provide results of series of extensive experiments in scheduling divisible load application in a Cloud environment to decrease the overall application execution time considering the cloud networking and computing capacities presented to the application's user. We experiment with real applications within the Amazon cloud computing environment. Our extensive experiments analyze the reasons of the discrepancies between a theoretical model and the reality and propose adequate solutions. These discrepancies are due to three factors: the network behavior, the application behavior and the cloud computing virtualization. Our results show that applying the algorithm result in a maximum ratio of 1.41 of the measured normalized makespan versus the ideal makespan for application in which the communication to computation ratio is big. They show that the algorithm is effective for those applications in a heterogeneous setting reaching a ratio of 1.28 for large data sets. For application following the ensemble clustering model in which the computation to communication ratio is big and variable, we obtained a maximum ratio of 4.7 for large data set and a ratio of 2.11 for small data set. Applying the algorithm also results in an important speedup. These results are revealing for the type of applications we consider under experiments. The experiments also reveal the impact of the choice of the platforms provided by Amazon on the performance of the applications under study. Considering the emergence of cloud computing for high performance applications, the results in this paper can be widely adopted by cloud computing developers. Copyright © 2014 John Wiley & Sons, Ltd.     

Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing

Today, almost everyone is connected to the Internet and uses different Cloud solutions to store, deliver and process data. Cloud computing assembles large networks of virtualized services such as hardware and software resources. The new era in which ICT penetrated almost all domains (healthcare, aged-care, social assistance, surveillance, education, etc.) creates the need of new multimedia content-driven applications. These applications generate huge amount of data, require gathering, processing and then aggregation in a fault-tolerant, reliable and secure heterogeneous distributed system created by a mixture of Cloud systems (public/private), mobile devices networks, desktop-based clusters, etc. In this context dynamic resource provisioning for Big Data application scheduling became a challenge in modern systems. We proposed a resource-aware hybrid scheduling algorithm for different types of application: batch jobs and workflows. The proposed algorithm considers hierarchical clustering of the available resources into groups in the allocation phase. Task execution is performed in two phases: in the first, tasks are assigned to groups of resources and in the second phase, a classical scheduling algorithm is used for each group of resources. The proposed algorithm is suitable for Heterogeneous Distributed Computing, especially for modern High-Performance Computing (HPC) systems in which applications are modeled with various requirements (both IO and computational intensive), with accent on data from multimedia applications. We evaluate their performance in a realistic setting of CloudSim tool with respect to load-balancing, cost savings, dependency assurance for workflows and computational efficiency, and investigate the computing methods of these performance metrics at runtime.     

球对称n维反应扩散方程的边界观测与输出反馈控制

许多实际系统可用n 维超球坐标系来描述, 并且系统有球对称的性质, 因而可通过研究半径方向的状态变化, 得到系统的全局动态过程. 通过将高维的对称系统转化为等价的径向一维方程, 本文采用边界Backstepping 方法设计了球对称反应扩散方程的输出反馈控制器. 使用容易测量的边界状态值, 设计了状态观测器来估计系统在空间域的所有状态, 从而实现输出反馈控制. 本文扩展了连续Backstepping 方法,提出了n维球坐标的Volterra 积分映射, 从而求出了显式表达的控制器和状态观测器. 论文用Lyapunov 函数法证明了输出反馈系统在H1范数下指数稳定, 表明状态对初值的连续依赖, 确保控制系统具有较好的性质, 不会在空间某点发散. 最后进行了数值仿真, 仿真结果表明系统在输出反馈控制律的作用下收敛到稳态值.     

Categorization of risk factors for distributed agile projects

ContextOrganizations combine agile approach and Distributed Software Development (DSD) in order to develop better quality software solutions in lesser time and cost. It helps to reap the benefits of both agile and distributed development but pose significant challenges and risks. Relatively scanty evidence of research on the risks prevailing in distributed agile development (DAD) has motivated this study.ObjectiveThis paper aims at creating a comprehensive set of risk factors that affect the performance of distributed agile development projects and identifies the risk management methods which are frequently used in practice for controlling those risks.MethodThe study is an exploration of practitioners’ experience using constant comparison method for analyzing in-depth interviews of thirteen practitioners and work documents of twenty-eight projects from thirteen different information technology (IT) organizations. The field experience was supported by extensive research literature on risk management in traditional, agile and distributed development.ResultsAnalysis of qualitative data from interviews and project work documents resulted into categorization of forty-five DAD risk factors grouped under five core risk categories. The risk categories were mapped to Leavitt’s model of organizational change for facilitating the implementation of results in real world. The risk factors could be attributed to the conflicting properties of DSD and agile development. Besides that, some new risk factors have been experienced by practitioners and need further exploration as their understanding will help the practitioners to act on time.ConclusionOrganizations are adopting DAD for developing solutions that caters to the changing business needs, while utilizing the global talent. Conflicting properties of DSD and agile approach pose several risks for DAD. This study gives a comprehensive categorization of the risks faced by the practitioners in managing DAD projects and presents frequently used methods to reduce their impact. The work fills the yawning research void in this field.     

农业海量信息资源管理平台设计与实现

随着农业信息化的深入,如何描述、存储、管理和处理海量分布信息资源成为农业应用的首要问题。提出基于XML/XML Schema的统一农业信息资源描述框架,将XML描述与元数据约束相结合,采用层次化管理结构实现元数据与资源信息的组织;在HDFS(Hadoop Distributed File System)云存储架构适应性改进基础之上,设计实现了农业海量信息资源管理平台,实现了海量、分布、异构资源的分类组织、存储和访问服务。平台覆盖了全国6个省市资源节点,实现了3.2 TB农业资源存储管理,与传统资源检索方法相比,平台资源检索时间缩短73%;同时,用户在同一检索条件下能更多地获得相关资源,不仅查准率平均提高5%,而且资源利用率平均提高17.7%。平台在海量资源检索和利用效率方面获得提升。