网格服务实现HLA/RTI向Intemet环境的扩展

基于HLA的分布式仿真已得到广泛的应用,但组建需要跨越组织或基于公共互联网通信的基于HLA的分布式仿真应用目前还较难实现.网格计算技术在资源协调、管理方面的优势为分布式仿真提供了新的发展空间.本文在回顾基于网格技术的仿真的发展研究前沿前提下,提出了基于网格服务实现HLA/RTI向Intemet环境扩展的系统结构.系统结构在考虑融入网格技术优势之外,同时考虑到了仿真组件的可重用性、互操作性和兼容性等问题.原型系统仿真实验表明了系统结构的可行性.     

仿真网格中服务的最优化调度机制研究

在网格中,如何为任务提供最优化资源服务是一个十分复杂的问题.本文以仿真网格为基础,建立一种服务的最优化调度的理论模型.根据这种理论模型,提出一种新的遗传模拟退火算法.从而形成一种仿真网格环境下服务的最优化调度机制.为了验证这种机制的可行性和有效性,开发一个基于此服务调度机制的仿真网格运行管理系统.通过这个系统的仿真实验表明:这种服务的最优化调度机制具有良好的可行性和有效性.     

仿真网格体系结构及关键技术的研究与实现

网格是一个集成的资源与计算环境，是当前信息领域的研究热点。基于网格技术构建协同建模/仿真环境以及仿真网格，能在更大程度上支持各种仿真软硬件资源的共享，成为复杂系统建模与仿真的重要技术与工具。该文基于网格和Web技术构建层次化的仿真网格原型系统体系结构，对原型系统各个层次中涉及的仿真网格支撑平台技术、仿真网格仿真资源网格化技术、仿真网格服务管理技术以及仿真网格应用门户技术及其实现途径进行了研究与实现。     

基于网格的分布式仿真综述

分布式仿真已经广泛应用于科学研究、工程、商业等方方面面.HLA的提出是为了解决分布式仿真中不同类型的仿真模型、仿真应用模块之间的互操作和提高仿真组件的重用性,并没有涉及到仿真资源层的管理,所以在面对现在分布式仿真向大规模发展和需要使用在地理位置上处于分布式的计算资源和数据资源时,存在一定的局限.基于网格的分布式仿真是一门刚刚起步的新兴技术,它致力于将传统的分布式仿真移植到网格环境中,以利用网格技术的各项优势.文中介绍了分布式仿真与网格技术的发展与应用现状,随后着重介绍基于网格的分布式仿真的研究发展前沿,并分析讨论了其面临的问题和发展的方向,为进一步的研究提供一定的指导性帮助.     

面向科学计算的网格环境

为了充分整合分布的高性能计算资源,本文提出一种面向科学计算的网格环境,旨在形成一个可统一管理和运行维护的虚拟的超级计算机资源,面向用户提供统一、易用、可靠的科学计算服务。面向科学计算的网格环境通过轻量级网格中间件SCE汇聚资源,支持作业的全局调度、数据的统一管理视图,面向用户提供命令行和网格门户两种使用方式,并提供编程接口供专业社区和学科平台二次开发使用,满足不同层次的用户需求。目前,面向科学计算的网格环境已经在中国科学院超级计算环境(ScGrid)中得到应用和用户认可。     

多用户协同虚拟装配网格系统研究

针对复杂产品协同虚拟装配仿真应用中存在的协同交互能力差、计算资源短缺、资源动态调度分配难、规模易受限制等问题,提出了一种借鉴网格体系结构的多用户协同虚拟装配网格平台方案,介绍了系统中仿真计算资源的类型及功能,进而提出了面向服务的仿真计算资源服务化封装方法,同时还给出了系统资源管理、任务管理与调度等部分关键技术的解决方案.最后将原型系统用于某汽车多用户异地协同装配仿真验证了该系统的有效性.     

网格计算下大规模虚拟仿真环境应用研究

长久以来大规模虚拟仿真环境由于受到其对计算、存储、实时性等高要求的束缚未能得到广泛的应用.文中研究了网格计算技术的特点,并列出了大规模虚拟仿真环境应用与网格计算技术结合后的优势,接着分别从资源获得、时钟管理、实体交互三个方面分析了大规模虚拟仿真环境应用所存在的问题,并以解决这些问题为目的引入了网格计算技术,提出了在网格计算环境下对大规模虚拟仿真应用这些问题的解决方案.     

面向汽车耐撞性的协同设计与仿真网格

汽车耐撞性设计是一个典型的协同设计工程,其中CAD/CAE集成和大规模计算是企业需要解决的难题之一.网格计算因其具有面向服务、可以屏蔽异构资源复杂性的特点,为实现汽车耐撞性协同设计提供了可能的技术方案.在分析了耐撞性设计需求的基础上,设计了一个4层的汽车耐撞性协同设计与远程仿真的网格系统架构,并讨论了设计原理、工作流程和相关工具等.通过将资源封装为标准的网格应用中间件和服务,简化了异构资源的集成和管理.该系统实现了不同设计人员在同一环境和平台上的协同,同时保持参与各方各自的独立性.最后应用一个座椅与整车的协同设计实验,验证了文中系统的有效性.     

计算作业网格执行环境的分析、设计与应用*

针对网格环境下科学计算遗产程序执行涉及到的关键问题进行了分析和设计,提出了一个基于固有计算执行环境+网格运行支持扩展的计算作业网格执行环境,以支持在网格环境下调用执行科学程序,并将其应用到了科学计算遗产程序包装成的网格服务运行中。计算作业网格执行环境很好地满足了传统科学计算遗产程序在网格环境下共享后的运行管理的需要,具有广泛的适用性和参考价值。     

应用Globus Toolkit实现网格服务

Globus对信息安全、资源管理、信息服务、数据管理以及应用开发环境等网格计算的关键理论和技术进行了广泛的研究,开发出能在多种平台上运行的网格计算工具包Globus Toolkit.此工具包能够用来帮助规划和组建大型的网格实验和应用平台,开发适合大型网格系统运行的应用程序.本文分析网格计算软件Globus Toolkit 3（GT3）的体系结构以及安装方法,然后以一个简单的网格计算为例在基于网格计算软件GT3的网格平台上实现网格服务.     

Interference Analysis of Co-Located Container Workloads: A Perspective from Hardware Performance Counters

Workload characterization is critical for resource management and scheduling.Recently,with the fast development of container technique,more and more cloud service providers like Google and Alibaba adopt containers to provide cloud services,due to the low overheads.However,the characteristics of co-located diverse services(e.g.,interactive on-line services,off-line computing services)running in containers are still not clear.In this paper,we present a comprehensive analysis of the characteristics of co-located workloads running in containers on the same server from the perspective of hardware events.Our study quantifies and reveals the system behavior from the micro-architecture level when workloads are running in different co-location patterns.Through the analysis of typical hardware events,we provide recommended/unrecommended co-location workload patterns which provide valuable deployment suggestions for datacenter administrators.     

基于线索化方法的嵌入式Java虚拟机性能优化技术研究

随着计算机的不断发展，逐渐呈现出了普适计算的模式．普遍认为，Java是适应普适计算的关键技术．分析了解释运行中利用线索化方法进行性能优化的技术，实现了基于直接线索化方法的嵌入式Java虚拟机的解释器性能优化方案，并对嵌入式Java虚拟机的参考实现和基于直接线索化的优化方案进行了性能对比．     

A solution of multidisciplinary collaborative simulation for complex engineering systems in a distributed heterogeneous environment

This paper presents an integrated approach to multidisciplinary collaborative simulation for complex engineering systems. The formulized paradigm of multidisciplinary collaborative simulation for com- plex engineering systems is principally analyzed. An IEEE HLA and web services based framework is proposed to provide a heterogeneous, distributed and collaborative running environment where multidisciplinary modeling, running management and post-processing of collaborative simulation are undertaken. The mecha...     

An Effective Model and Management Scheme of Personal Space for Ubiquitous Computing Applications

In ubiquitous computing, the computing environment for a user is no longer a fixed computer, but a space that includes multiple heterogeneous devices that can change dynamically according to the user's situation. Managing the space is an essential part of ubiquitous computing because application services in this environment need to be adaptive to the users' current situation. However, previous approaches oversimplified the model of personal space and demonstrated some limitations in developing user-centric adaptive services. In this paper, we propose an effective personal space model, defined as virtual personal world (VPW), and a sophisticated method to manage personal spaces. The VPW represents a personal space by using a set of stateful elements and their relationships, which are denoted as virtual objects, services, and neighbors. The VPW provides expressive and accurate information for a particular user, thereby helping application services adapt their operations for the user dynamically. Our conceptual model is designed as personal operating middleware software that manages the user's VPW and provides application services. Experimental results show that the prototype system based on VPW has reasonable performance in running application services and managing personal spaces. We also found that the VPW model can increase the average user satisfaction rate by up to 40% compared to other models in our simulation environment.      

CWS服务平台的设计与实现——一个基于Web服务技术的计算资源共享系统

Web服务和网格计算技术,出现了日益融合的趋势,网格计算已经进入了服务为中心的新阶段。在这种情况下,就可以在具有良好跨平台性和互操作性的Web服务框架上实现计算资源的共享。论文提出了CWS服务和支撑CWS服务注册发布和共享运行的平台系统。在平台系统实现的过程中解决了几个关键问题。目前,已在北京大学计算机系的校园网内搭建了CWS服务平台,成功运行了多个服务实例。     

Investigation on runtime partitioning of elastic mobile applications for mobile cloud computing

The latest developments in mobile computing technology have increased the computing capabilities of smartphones in terms of storage capacity, features support such as multimodal connectivity, and support for customized user applications. Mobile devices are, however, still intrinsically limited by low bandwidth, computing power, and battery lifetime. Therefore, the computing power of computational clouds is tapped on demand basis for mitigating resources limitations in mobile devices. Mobile cloud computing (MCC) is believed to be able to leverage cloud application processing services for alleviating the computing limitations of smartphones. In MCC, application offloading is implemented as a significant software level solution for sharing the application processing load of smartphones. The challenging aspect of application offloading frameworks is the resources intensive mechanism of runtime profiling and partitioning of elastic mobile applications, which involves additional computing resources utilization on Smart Mobile Devices (SMDs). This paper investigates the overhead of runtime application partitioning on SMD by analyzing additional resources utilization on SMD in the mechanism of runtime application profiling and partitioning. We evaluate the mechanism of runtime application partitioning on SMDs in the SmartSim simulation environment and validate the overhead of runtime application profiling by running prototype application in the real mobile computing environment. Empirical results indicate that additional computing resources are utilized in runtime application profiling and partitioning. Hence, lightweight alternatives with optimal distributed deployment and management mechanism are mandatory for accessing application processing services of computational clouds.     

Building Model as a Service to support geosciences

Modeling is a fundamental methodology for simulating the past, understanding the present and predicting the future of the geospatial systems and phenomena. However, modeling in the geospatial science poses several challenges, including complex model setup, repetition in model setup, requirement for large, scalable computing resources, and management of a large amount of model output. To address these challenges, we propose Model as a Service (MaaS) by leveraging the latest advancement of cloud computing. MaaS enables various geoscience models to be published as services, and these services can be accessed through a simple web interface. MaaS automates the processes of configuring machines, setting up and running models, and managing model outputs. The computing resources are automatically provisioned by MaaS in a cloud environment. A proof-of-concept MaaS prototype is presented using a global climate change model (ModelE). Experimental results show that the MaaS prototype significantly simplifies model setup, accelerates model simulation and enhances model output by providing a web-based, on-demand, scalable modeling environment.     

The Legion support for advanced parameter-space studies on a grid

Parameter-space (p-space) studies involve running a single application several times with different parameter sets. Since the jobs are mutually independent, many computing resources can be recruited to conduct an entire study in a distributed manner. The p-space studies are attractive applications for grids, which are networked collections of computing and other resources. Legion is a grid infrastructure that facilitates the secure and easy use of heterogeneous, geographically distributed resources by providing the illusion of a single virtual machine from those resources. Legion provides tools and services that support advanced p-space studies, i.e., studies that make complex demands such as transparent access to distributed files, fault-tolerance and security. We demonstrate these benefits with a protein-folding experiment in which a molecular simulation package was run over a grid managed by Legion.     

Outlook: Cloudy with a Chance of Security Challenges and Improvements

Cloud computing presents an opportunity to offload computing to third party resources, but this business model isn't without security risks. Customers must determine if running their computing on a base system managed by a third party is better than running on their own systems. They must be convinced that running in the same data center as their competitors or adversaries is safe, and they are still responsible for complex security configuration. Despite these concerns, cloud vendors can also leverage the cloud architecture to improve security by building verifiable base systems, designing integrity-protected cloud services, and limiting security configuration complexity. Thus, while the cloud architecture provides security challenges, there are also opportunities to ease the security configuration burden on customers.     

Research on the collaboration of service selection and resource scheduling for IoT simulation workflows

Cloud resources provide a promising way to efficiently perform the needed simulation tasks for a complex manufacturing process. Most of the existing work focuses only on how to effectively schedule computing resources to execute computing requirements of simulation workflows in Internet of Things (IoT) applications. Research on the scheduling of simulation workflows in consideration of task ordering, service selection, and resource allocation altogether has not been lacking. To fill in this void, this paper proposes a cloud-based 3-stage workflow scheduling model. Before scheduling computing resources to complete task requirements, the order of the tasks is determined and the services that can meet the task requirements are selected. In this model, the workload to satisfy task requirements is not fixed and takes on a different value depending upon the service selected with its unique complexity and accuracy. An optimization function that transforms and integrates makespan, cost, and accuracy in a unique way is proposed. For its solution, the relatively new symbiotic organisms search (SOS) algorithm is modified and two SOS-based optimization strategies are developed, i.e., joint optimization-based SOS (JOSOS) and split optimization-based SOS (SOSOS). The simulation results reveal that SOS-based algorithms, especially the SOSOS method, outperform all compared algorithms. Based on the proposed method, simulation services and computing resources can be rationally selected and scheduled to ensure the requirements of IoT applications.