一种分布对象的并行程序设计框架

计算性能和合成性能对于基于工作站网的软件十分重要,但由于缺乏相应的开发环境,现在这类软件在这两方面还做得很不够,尤其是合成性能十分薄弱.该文提出并实现了一种基于分布对象的并行程序设计框架,力图使分布对象能提供高性能的并行计算服务,同时也使并行算法获得一种良好的封装和复用机制.经过一些并行算法的测试,表明该框架具有实用价值.     

Performance measurement for parallel and distributed programs: astructured and automatic approach

Novel approaches are presented for designing performance measurement systems for parallel and distributed programs. The first approach involves unifying performance information into a single, regular structure that reflects the structure of programs under measurement. The authors define a hierarchical model for the execution of parallel and distributed programs as a framework for the performance measurement. A complete different levels of detail in the hierarchy. The second approach is based on the development of automatic guidance techniques that can direct users to the location of performance problems in the program. Guidance information from such techniques supplies facts about problems in the program and provides possible answers for the further improvement of program efficiency. A performance measurement system, called IPS, has been developed as a prototype of the authors' model and design. Some of the test results from IPS are also discussed     

Application Security Models for Mobile Agent Systems

Mobile agents are a distributed computing paradigm based on mobile autonomous programs. Mobile applications must balance security requirements with available security mechanisms in order to meet application level security goals. We introduce a trust framework to reason about application security requirements, trust expression, and agent protection mechanisms. We develop application security models that capture initial trust relationships and consider their use for mobile agent security.     

无人驾驶坦克自主作战决策系统框架

提出了一个分布式解决无人驾驶坦克自主作战决策的框架，将自主作战、动态任务规划和任务策略的智能评估等思想引入无人驾驶坦克自主作战决策系统。包括以下两个重要内容：主体个体的能力和职责；信息和知识如何在主体之间传播使得他们能准确地完成作战任务。文中重点是无人驾驶坦克自主作战决策系统中主体之间动态知识的交换。主体确保自己的行为是一种动态的经验操作和直接与其他主体交流事实、需求和规则的结合。在各个主体中，知识可以作为事实、规则、需求的结合体来传播交换。     

CLAP: Collaborative pattern mining for distributed information systems

The purpose of data mining from distributed information systems is usually threefold: (1) identifying locally significant patterns in individual databases; (2) discovering emerging significant patterns after unifying distributed databases in a single view; and (3) finding patterns which follow special relationships across different data collections. While existing research has significantly advanced the techniques for mining local and global patterns (the first two goals), very little attempt has been made to discover patterns across distributed databases (the third goal). Moreover, no framework currently exists to support the mining of all three types of patterns. This paper proposes solutions to discover patterns from distributed databases. More specifically, we consider pattern mining as a query process where the purpose is to discover patterns from distributed databases with patterns' relationships satisfying user specified query constraints. We argue that existing self-contained mining frameworks are neither efficient, nor feasible to fulfill the objective, mainly because their pattern pruning is single-database oriented. To solve the problem, we advocate a cross-database pruning concept and propose a collaborative pattern (CLAP) mining framework with cross-database pruning mechanisms for distributed pattern mining. In CLAP, distributed databases collaboratively exchange pattern information between sites so that each site can leverage information from other sites to gain cross-database pruning. Experimental results show that CLAP fits a niche position, and demonstrate that CLAP not only outperforms its other peers with significant runtime performance gains, but also helps find patterns incapable of being discovered by others.     

大数据存储架构和算法研究综述

随着大数据计算需求的增长，集群的处理速度需要得到快速的提升，然而目前大数据处理框架的处理性能已逐渐满足不了这种快速增长的需求。由于集群的存储架构是分布式存储，因此数据的存放在大数据处理过程中成为影响集群的处理性能的因素之一。首先，对当今的分布式文件存储系统的结构进行了介绍；接着，根据不同的优化目标，例如减少网络负载、负载均衡、降低能耗和高容错性等，对近年国内外大数据存储算法的研究进行了总结，分析和对比了已有算法的优点以及存在的问题；最后，对大数据存储架构和优化算法设计的挑战和未来研究方向作了展望。     

一个新的综合集成研讨厅软件框架

综合集成研讨厅是一种用于研究开放复杂巨系统的方法论,目前已在经济、军事等领域得到了应用。介绍了一种新的综合集成研讨厅系统的软件框架设计。首先提出了系统的整体设计目标;其次介绍了基于流程的灵活的研讨组织框架,它是整个软件架构的核心;然后设计了基于Plugin设计模式的通用工具、模型、知识库管理框架;最后给出了分布式决策支持工具的通信机制和远程调用过程。     

An autonomic approach for P2P/cloud collaborative environments

The rise of Cloud Computing has progressively dimmed the interest in volunteer and peer-to-peer computing, in general. However, efficient and cost-effective large scale distributed collaborative environments cannot be achieved leveraging upon the Cloud alone. In this paper, we propose a novel hybrid P2P/cloud approach where components and protocols are autonomically configured according to specific target goals, such as cost-effectiveness, reliability and availability. The proposed approach is based on the Networked Autonomic Machine (NAM) framework, which allows distributed system designers to include different kinds of cost and performance constraints. As an example, we show how the NAM-based approach can be used to design collaborative storage systems, enabling the definition of an autonomic policy to decide, according to cost minimization and data availability goals, how to part data chunks among peer nodes and Cloud, based on the local perception of the P2P network.