面向方面的MDA在分布式实时系统中的应用

由于复杂分布式实时系统中具有不同种类的资源,不同的种类资源其属性及调配策略不同,需要采取不同的技术建立不同的模型。传统的系统分析设计的方法难以保证开发的效率和质量。引入面向方面的MDA开发方法,为减低实时系统开发中的复杂性提供了可行的方法。     

基于UML的网格QoS面向方面建模方法

利用基于UML的面向方面编程(AOP)技术来建模实时系统,结合传统的网格QoS结构模型,把QoS关注从系统中抽象为一个独立于系统的QoS方面,从结构建模、行为建模、方面织入以及代码产生几个方面实现面向方面的建模方法,更好地提供网格环境下QoS保证,分离了QoS关注,提高软件的重用性.     

基于汽车电子实时系统的健康监控模块实现

随着汽车整车安全性、经济性等方面的发展,本文结合汽车电子软件平台化的需求,建立了车控实时系统健康管理方法理论架构和流程体系,实现了面向汽车电子实时系统的健康监控管理模块。本文中的健康监控模块通过AUTOSAR/OSEK系统服务API获取车控实时系统运行时任务、资源、事件、中断等软件状态信息,通过环境和应用系统状态检测器读取运行环境信息和车控应用对象的状态信息,利用相应的hook函数实现信息的监测、收集和发送。     

分布式处理在实时应用系统中的实现

实时系统是计算机应用的一个重要方面,许多实时系统由于检测和控制的设备在地理上的分散性,要实现集中式的计算机控制本身就有较大的难度.分布式处理则相对较灵活,同时具有较好的性能价格比和容错性,近年来发展迅速.该文以上海虹桥国际机场电力监控系统为例,介绍大型变配电站用电监控和管理系统设计中使用分布式处理技术的方法.     

基于中间件的QoS管理模型的研究与应用

随着多媒体处理技术和高速网络技术的发展,分布式多媒体应用得到长足发展,分布式多媒体中的实时音频、视频应用的一个关键问题就是多媒体的QoS管理和控制问题。该文提出了一种应用层的基于中间件的QoS管理模型,研究端系统的QoS和资源管理。该框架允许用户和应用对QoS管理策略和适应性调整策略进行应用级的定制。文章在资源管理中提出了通用资源接口及其具体实现方法。并从应用层的角度,结合国内外的研究成果,在网络层和操作系统之上提出通用接口应具备的功能,以及如何与QoS管理器协调工作,及其简单实现。     

面向方面的分布式实时软件性能分析

性能是横切分布式实时系统的关注点,面向方面技术可以将性能关注点模块化,为性能分析提供了优越的实现条件。该文提出一个面向方面的性能工程框架,利用面向方面扩展的多种视图并结合规格化的参数描述,针对面向方面软件进行性能建模与测量。分析了一个分布式Web应用的例子及其性能瓶颈所在,结果表明该框架是可行有效的。     

一个具有服务质量保障的面向服务的网格模型

为了在面向服务的网格系统中提供服务质量（QoS）支持，本文较深入地研究了OGSA及Web Service等相关理论和技术．基于面向服务的体系结构，提出了一个具有QoS保障的面向服务的网格系统模型SoGSM—QoS．该模型通过物理网格节点的联合，来消除管理上的冗余并简化网格系统结构．模型支持虚拟组织和资源虚拟化，在各层次间提供用户QoS需求的映射和监控，保障用户的QoS．本文详细阐述了模型的结构、功能和访问控制机制，并建立了相应的过程模型．分析了有关性能，验证了模型的有效性．     

基于AOP的QoS中间件自适应机制研究

异构动态实时系统中复杂的应用层QoS参数分解为多个操作系统和网络支持的QoS参数,并映射为操作系统和网络无法提供的实现机制,QoS中间件需要实现这些机制。本文首先讨论了QoS中间件的体系结构,进一步给出了一种基于面向方面的编程技术AOP的QoS中间件自适应机制。采用这种机制,通过间接增加一个额外层,将服务中的所有组件编织到一个自适应方面里。     

实时系统的面向方面模型

基于UML的实时系统面向方面模型,能够把实时关注从系统中分离出来,形成一个独立于系统的时间方面,实现时间方面的并发设计和系统时间特性的统一管理。面向方面编程（AOP）技术允许把设计好的时间方面根据特定需要重新织入系统,组合为实时系统。模型扩展了UML来表达AOP技术和时间概念,并从系统的静态结构模型、动态行为模型和时间方面的织入等几部分建模实时系统。一个电梯控制系统例子充分说明这种设计过程。     

多维QoS在基于MOM的QoS管理模型中的应用

服务质量QoS管理是满足分布式环境中端到端QoS需求的关键,而中间件是一种分布式系统中的主要交互平台.首先对中间件和QoS的相关概念进行介绍,在分析传统QoS管理模型的基础上,提出一种基于消息中间件(MOM)的QoS管理模型,重点分析多维QoS在该模型中的应用,最后结合一个实例说明多维QoS在实际系统中的应用.     

基于YARN的分布式资源动态调度与协同分配系统

Storm on YARN是目前主流的分布式资源调度框架,但其存在需要人工干预和无法根据资源可用性实时调整系统资源的不足。根据流数据处理的实时延迟计算系统负载情况,在Storm平台上基于YARN设计分布式资源调度和协同分配系统。建立包含系统层和任务层的双层调度模型,系统层通过对流数据处理负载的实时监测进行资源分配预测,任务层利用ZooKeeper和YARN对集群资源的高效管理能力进行动态资源管理。实验结果表明,该系统可以实时调整集群资源分布,有效减小系统延迟。     

Designing a fuzzy system for controlling the armament fire in dynamic siege

This paper presents a fuzzy system for controlling the fire command in a surface-to-surface engagement by assuming siege of friendly side by enemy forces. The system must be capable of interacting with a dynamic and uncertain battle world in a real-time manner. In siege situation, it is assumed that the enemy troops are advancing toward friendly side in different directions and the defence resources are limited and nonrenewable at a single platform. Thus, resource allocation problem in a real-time manner is an important and vital component of battle management until coming of auxiliary forces. This paper addresses the design of a fuzzy system as an efficient tool for real-time decision-making in order to optimize the defense resource allocation in engagement.     

Developing security-aware resource management strategies for workflows

This paper investigates the resource allocation problem for a type of workflow in pervasive computing. These workflows are abstracted from the enterprise-level applications in the business or commerce area. The activities in these workflows require not only computing resources, but also human resources. Human involvement introduces additional security concerns. When we plan/allocate resource capacities, we often assume that when a task is allocated to a resource, the resource will accept the task and start the execution once the processor becomes available. However, the security policies impose further constraints on task executions, and therefore may affect both application- and system-oriented performance. Authorization is an important aspect in security. This paper investigates the issue of allocating resources for running workflows under the role-based authorization control, which is one of the most popular authorization mechanisms. By taking into account the authorization constraints, the resource allocation strategies are developed in this paper for both human resources and computing resources. In the allocation strategy for human resources, the optimization equation is constructed subject to the constraint of the budget available to hire human resources. Then the optimization equation is solved to obtain the number of human resources allocated to each authorization role. The allocation strategy for computing resources calculates not only the number of computing resources, but also the proportion of processing capacity in each resource allocated to serve the tasks assuming each role. The simulation experiments have been conducted to verify the effectiveness of the developed allocation strategies. The experimental results show that the allocation strategy developed in this paper outperforms the traditional allocation strategies, which do not consider authorization constraints, in terms of both average response time and resource utilization.     

嵌入式分布实时系统自适应资源管理架构

分析了开放嵌入式分布实时(Distributed Real-time and Embedded,DRE)系统的自适应资源管理需求,针对传统静态资源管理方法的不足,提出了一种面向开放DRE系统的自适应资源管理架构;该架构采用动态任务管理、实时资源分配和自适应控制三种关键技术,能够更加适用于开放、动态的环境,有效应对资源约束和资源状态、应用程序QoS的变化;提高系统性能和稳定性;屏蔽底层硬件资源的异构性,为上层应用提供统一的服务接口。     

基于和声算法异构Hadoop集群资源分配优化

异构Map-Reduce环境中资源分配策略直接影响其响应时间,如何利用有效的策略将计算任务分配到计算资源是亟待解决的问题。利用和声搜索算法对异构Hadoop集群中的计算资源分配问题进行优化。对问题进行建模时考虑了异构计算机集群中各节点的处理能力、带宽和线路质量和源数据位置等因素对计算资源分配的影响,利用和声搜索算法优化资源分配策略,以期在满足用户需求的前提下提高系统的响应时间。并用Gridsim对算法进行仿真实验,实验结果表明利用和声搜索算法可以达到减少系统响应时间的目的。     

RAPS: a rule-based language for specifying resource allocation andtime-tabling problems

A general language for specifying resource allocation and time-tabling problems is presented. The language is based on an expert system paradigm that was developed previously by the authors and that enables the solution of resource allocation problems by using experts' knowledge and heuristics. The language enables the specification of a problem in terms of resources, activities, allocation rules, and constraints, and thus provides a convenient knowledge acquisition tool. The language syntax is powerful and allows the specification of rules and constraints that are very difficult to formulate with traditional approaches, and it also supports the specification of various control and backtracking strategies. We constructed a generalized inference engine that runs compiled resource allocation problem specification language (RAPS) programs and provides all necessary control structures. This engine acts as an expert system shell and is called expert system for resource allocation (ESRA). The performance of RAPS combined with ESRA is demonstrated by analyzing its solution of a typical resource allocation problem     

云计算弹性评测模型的研究与实现

当前,越来越多的企业开始将自己的核心业务与数据迁移到云上,其中很多业务需要相应的弹性服务来应对负载的实时变化,因此对弹性的评测正变得越来越重要,然而当前缺少一种较为全面的弹性评测方法。为解决以上问题,从资源分配、QoS、资源配置时间等多个角度,对云计算的弹性进行较为全面的分析,提出适用于供应商和用户两个角度的评测方法。在已有基础上,提出资源分配、资源配置时间两个方面的计算模型,并对现存的罚金模型进行改进。最后,在CloudStack云平台上,使用auto-scaling和scale-out两种弹性扩展策略,以TPC-W为负载验证了所提方法的有效性。     

数字孪生甬江流域水资源“四预”应用研究

基于当前数字孪生流域发展背景及宁波市水资源利用特点，为满足宁波市水资源在不同时空下合理分配的需求，以甬江流域水资源管理与调配“四预”流程为切入点，深度融合数字孪生、BIM 建模等现代化信息技术，在建立来水预报分析、需水预测、水资源优化调配、水资源实时分析评价及水资源预警等各类模型的基础上， 构建具有“预报分析—监测预警—调配预演—调度预案—动态评价”功能的水资源管理与调配业务应用系统，以达到及时准确预报、全面精准预警、同步仿真预演、精细数字预案、多维动态评价的目标，最终实现水资源的智能优化调配与管理。     

A game theory-based dynamic resource allocation strategy in Geo-distributed Datacenter Clouds

Geo-distributed Datacenter Cloud is an effective solution to store, process and transfer the big data produced by Internet-of-Things (IoT). A key challenge in this distributed system is how to allocate the bandwidth resources among these geo-distributed datacenters of this cloud efficiently. This paper aims to address this challenge by optimizing the transfer bandwidth resources among different geo-distributed datacenters. To this end, we firstly analyze the interaction between the traffic of physical networks and the data flow of Geo-distributed Datacenter Clouds, and then establish a game theory-based model for cloud resource allocation. Based on this model, a dynamic resource allocation strategy and its corresponding algorithm that are adaptable to the Internet conditions are proposed. Since the background traffic, capacity limit of physical networks as well as the flows and resource demands of geo-distributed datacenters are taken into account, this new strategy can achieve the load balance of the physical networks and content transferring among different geo-distributed datacenters effectively. The real-world trace data is adopted to validate the effectiveness and efficiency of the proposed resource allocation strategy. Compared with existing strategies, the evaluation results demonstrate that our proposed strategy can balance the workloads of physical networks, reduce the response delay of cloud applications, and possess an excellent adaptability.