新一代联邦运行管理工具的设计

复杂、大规模仿真联邦对联邦运行管理提出了全过程和全系统的要求,并且对管理功能的操作和信息的表示也提出了更高的要求。在原有运行管理工具的基础上,结合对当前典型运行管理工具的研究,设计了符合新需求的新一代联邦运行管理工具。新的设计采用套件以及框架组件的方式,得到了快速高效的实现,并在实用中得到检验。     

Online algorithms for advance resource reservations

We consider the problem of providing QoS guarantees to Grid users through advance reservation of resources. Advance reservation mechanisms provide the ability to allocate resources to users based on agreed-upon QoS requirements and increase the predictability of a Grid system, yet incorporating such mechanisms into current Grid environments has proven to be a challenging task due to the resulting resource fragmentation. We use concepts from computational geometry to present a framework for tackling the resource fragmentation, and for formulating a suite of scheduling strategies. We also develop efficient implementations of the scheduling algorithms that scale to large Grids. We conduct a comprehensive performance evaluation study using simulation, and we present numerical results to demonstrate that our strategies perform well across several metrics that reflect both user- and system-specific goals. Our main contribution is a timely, practical, and efficient solution to the problem of scheduling resources in emerging on-demand computing environments.     

统一数据管理空间在大规模并行作业计算中的应用

大规模并行作业的计算通常涉及海量的计算数据和众多的高性能计算设备.随着网格计算技术帮助人们进行计算的同时,大规模并行作业的数据规模的增长也越来越快,对计算速度的要求也越来越高,为了充分利用网格等计算平台上的资源,提高作业的计算效率,人们通常需要将待计算的数据进行分组,然后分别上传至不同的平台上进行计算,这对科学研究和数据管理造成了极大的不便.本文提出了一个针对大规模并行作业计算的统一数据管理空间,实现了异构网格和计算平台上数据的逻辑整合,从而大大提高了对计算数据的管理效率,加快了科学活动的进程.本文最后通过统一数据管理空间在大规模虚拟筛选中的应用,对该统一空间的数据传输效率和数据管理能力进行了分析.     

Peer-to-peer service provisioning in cloud computing environments

This paper aims to advance the management and delivery of services in large, heterogeneous, uncertain, and evolving cloud computing environments. The goal is important because such systems are becoming increasingly popular, yet existing service management methods do not scale well, and nor do they perform well under highly unpredictable conditions. If these problems can be solved, then Information Technology (IT) services can be made to operate in more scalable and reliable manner. In this paper, we present a peer-to-peer approach for managing services in large scale, dynamic, and evolving cloud computing environments. The system components such as virtualized services, computing servers, storage, and databases self-organize themselves using a peer-to-peer networking overlay. Inter-networking system components through peer-to-peer routing and information dissemination structure is essential to avoid the problems of management bottleneck and single point of failure that is predominantly associated with traditional centralized and hierarchical distributed (grids/clouds) system design approaches. We have validated our approach by conducting a set of rigorous performance evaluation study using the Amazon EC2 cloud computing environment. The results prove that managing services based on peer-to-peer routing and information dissemination structure is feasible and offers significant performance benefits as regards to overall system reliability, scalability, and self-management.     

A taxonomy and survey of grid resource management systems for distributed computing

The resource management system is the central component of distributed network computing systems. There have been many projects focused on network computing that have designed and implemented resource management systems with a variety of architectures and services. In this paper, an abstract model and a comprehensive taxonomy for describing resource management architectures is developed. The taxonomy is used to identify approaches followed in the implementation of existing resource management systems for very large‐scale network computing systems known as Grids. The taxonomy and the survey results are used to identify architectural approaches and issues that have not been fully explored in the research. Copyright © 2001 John Wiley & Sons, Ltd.     

云应用部署配置模型

云计算已经成为广泛使用的计算范型,越来越多的大规模分布式系统已经或正在向云平台部署和迁移.用户在部署和管理维护应用系统时通常需要管理底层基础设施资源细节,或者使用平台提供方的应用部署和管理服务,前者使得应用部署和运行时管理易于出错且费时费力,而后者则降低了系统管理的灵活性,很难满足用户的个性化需求.针对这一问题,本文提出了一种高层抽象模型来描述云应用的部署配置和管理需求.需求模型采用声明式机制定义期望的系统状态,而无需描述实现目标状态所需的执行步骤和细节.本文基于开源云计算平台OpenStack和自动化配置管理工具Puppet进行了原型实现,通过一个应用案例验证模型的有效性.     

Modeling and managing the variability of Web service-based systems

Web service-based systems are built orchestrating loosely coupled, standardized, and internetworked programs. If on the one hand, Web services address the interoperability issues of modern information systems, on the other hand, they enable the development of software systems on the basis of reuse, greatly limiting the necessity for reimplementation. Techniques and methodologies to gain the maximum from this emerging computing paradigm are in great need. In particular, a way to explicitly model and manage variability would greatly facilitate the creation and customization of Web service-based systems. By variability we mean the ability of a software system to be extended, changed, customized or configured for use in a specific context.We present a framework and related tool suite for modeling and managing the variability of Web service-based systems for design and run-time, respectively. It is an extension of the COVAMOF framework for the variability management of software product families, which was developed at the University of Groningen. Among the novelties and advantages of the approach are the full modeling of variability via UML diagrams, the run-time support, and the low involvement of the user. All of which leads to a great deal of automation in the management of all kinds of variability.     

支持并行设计的产品数据管理

产品数据管理是基于数据库,分布计算和网络通讯等技术发展起来的新型工程数据管理技术,是有效管理和控制所有与产品有关的信息的并行设计框架,本文研究了支持大型电子设备并行设计的产品数据管理系统ＹＣ－Ｂｒｉｄｇｅ的开放式体系结构,并介绍了它的主要功能,实现方法及技术特点。     

普适计算中间件技术的研究与进展

当前，普适计算已经成为计算机科学中一个极具活力和影响力的研究领域。普适计算环境规模很大并且具有高度异构性，如网络架构的异构性、硬件平台的异构性、操作系统的异构性、应用服务的异构性等，而普适计算中间件技术可以解决异构性和跨平台特征，提供不同服务的集成应用，因此成为普适计算中的一个研究热点。基于此，本文对于目前国外关于普适计算中间件技术的研究现状做了一个总结；通过分析和比较，给出了普适计算中间件的设计原则；探讨了普适计算中间件技术的未来发展方向。     

基于云计算模式的ERP企业管理信息系统分析

对于现代企业来讲,为了实现对企业信息的有效管理,提高企业管理效率,已经开始致力于建立企业内部的ERP管理信息系统。目前国内的大中型企业,很多已经建立了完善的ERP企业管理信息系统。考虑到企业规模的扩大以及企业管理数据的成倍增加,现有的ERP管理信息系统已经显示出一定的局限性。随着计算机技术的飞速发展,云计算的概念逐步得到了推广和应用。基于云计算的特点,ERP企业管理信息系统开始有意识的与云计算概念相结合。本文主要介绍了云计算模式的ERP企业管理信息系统特点和优势,对其应用效果进行了深入的分析。     

面向普适计算环境的资源描述方法

普适计算环境下的资源描述是支持各项普适计算资源管理活动的基础,普适计算环境的开放性、面向用户特点和固有的不确定性因素对资源描述方法提出了新的要求。现有研究大多针对资源描述的服务化、语义化和用户化需求部分,对如何描述资源的不确定性信息支持不足。在语义Web服务描述OWL-S的基础上,增加对随机性信息表示的支持,扩展为新的OWL-SP。新方法较为全面地支持了普适资源描述的关键需求（服务化、语义化、用户化和随机化需求）,构成了一个较为完整的普适资源描述,可作为普适资源管理活动的基础。     

异构型计算与并行程序设计环境

异构型计算能有效地利用多种不同的高性能计算机，以满足大型计算问题的不同计算需求。本文着重讨论了支持异构型计算的并行程序设计环境所需具备的功能，并以目前最流行的ＰＶＭ和Ｅｘｐｒｅｓｓ系统为例进行说明。     

A characterization of workflow management systems for extreme-scale applications

Automation of the execution of computational tasks is at the heart of improving scientific productivity. Over the last years, scientific workflows have been established as an important abstraction that captures data processing and computation of large and complex scientific applications. By allowing scientists to model and express entire data processing steps and their dependencies, workflow management systems relieve scientists from the details of an application and manage its execution on a computational infrastructure. As the resource requirements of today’s computational and data science applications that process vast amounts of data keep increasing, there is a compelling case for a new generation of advances in high-performance computing, commonly termed as extreme-scale computing, which will bring forth multiple challenges for the design of workflow applications and management systems. This paper presents a novel characterization of workflow management systems using features commonly associated with extreme-scale computing applications. We classify 15 popular workflow management systems in terms of workflow execution models, heterogeneous computing environments, and data access methods. The paper also surveys workflow applications and identifies gaps for future research on the road to extreme-scale workflows and management systems.     

Riding Out the Storm: How to Deal with the Complexity of Grid and Cloud Management

Over the last decade, Grid computing paved the way for a new level of large scale distributed systems. This infrastructure made it possible to securely and reliably take advantage of widely separated computational resources that are part of several different organizations. Resources can be incorporated to the Grid, building a theoretical virtual supercomputer. In time, cloud computing emerged as a new type of large scale distributed system, inheriting and expanding the expertise and knowledge that have been obtained so far. Some of the main characteristics of Grids naturally evolved into clouds, others were modified and adapted and others were simply discarded or postponed. Regardless of these technical specifics, both Grids and clouds together can be considered as one of the most important advances in large scale distributed computing of the past ten years; however, this step in distributed computing has came along with a completely new level of complexity. Grid and cloud management mechanisms play a key role, and correct analysis and understanding of the system behavior are needed. Large scale distributed systems must be able to self-manage, incorporating autonomic features capable of controlling and optimizing all resources and services. Traditional distributed computing management mechanisms analyze each resource separately and adjust specific parameters of each one of them. When trying to adapt the same procedures to Grid and cloud computing, the vast complexity of these systems can make this task extremely complicated. But large scale distributed systems complexity could only be a matter of perspective. It could be possible to understand the Grid or cloud behavior as a single entity, instead of a set of resources. This abstraction could provide a different understanding of the system, describing large scale behavior and global events that probably would not be detected analyzing each resource separately. In this work we define a theoretical framework that combines both ideas, multiple resources and single entity, to develop large scale distributed systems management techniques aimed at system performance optimization, increased dependability and Quality of Service (QoS). The resulting synergy could be the key to address the most important difficulties of Grid and cloud management.     

Trust management in ubiquitous computing: A Bayesian approach

Designing a trust management scheme that can effectively evaluate the relationships among devices in pervasive computing environments is a challenging task. This paper continues the investigation of our recently proposed probabilistic trust management scheme for pervasive computing environments. We argue that in addition to allowing a device to find other appropriate devices with which to interact, while detecting those that are malicious, our trust management scheme is also capable of (1) allowing a device to judge the trustworthiness of another device it interacts with, while making a better use of the received recommendations and (2) behaving as expected when a device has little or enough experience of interactions with other devices and changes dynamically occur in the proportion of malicious devices. Simulation experiments are provided to assess the achievement of the stated goals, using some representative performance metrics.     

普适计算环境下基于构件化中间件的上下文管理技术研究

上下文相关技术已经成为普适计算环境中用于应用服务的不可或缺的核心技术之一。对于基于构件的中间件来说,对高性能的上下文管理的需求更是必不可少,因为它构成了普适计算环境中构件适配和部署的重要基础。因此,在已有构件中间件的基础上,我们针对普适计算环境的特点提出了一种整体的上下文管理方法,将上下文管理集成在已有的构件化中间件中。应用表明,该方法能够更好地支持普适计算环境下上下文相关的构件化应用开发。     

Simulation of SLA-based VM-scaling algorithms for cloud-distributed applications

Cloud Computing has evolved to become an enabler for delivering access to large scale distributed applications running on managed network-connected computing systems. This makes possible hosting Distributed Enterprise Information Systems (dEISs) in cloud environments, while enforcing strict performance and quality of service requirements, defined using Service Level Agreements (SLAs). SLAs define the performance boundaries of distributed applications, and are enforced by a cloud management system (CMS) dynamically allocating the available computing resources to the cloud services. We present two novel VM-scaling algorithms focused on dEIS systems, which optimally detect most appropriate scaling conditions using performance-models of distributed applications derived from constant-workload benchmarks, together with SLA-specified performance constraints. We simulate the VM-scaling algorithms in a cloud simulator and compare against trace-based performance models of dEISs. We compare a total of three SLA-based VM-scaling algorithms (one using prediction mechanisms) based on a real-world application scenario involving a large variable number of users. Our results show that it is beneficial to use autoregressive predictive SLA-driven scaling algorithms in cloud management systems for guaranteeing performance invariants of distributed cloud applications, as opposed to using only reactive SLA-based VM-scaling algorithms.     

On improving resource utilization and system throughput of master slave job scheduling in heterogeneous systems

The rapid advances of network technologies shed light on many aspects of the practicability of large scale ubiquitous computing. Grid technology has been recognized as an efficient solution to coordinate large-scale shared resources and execute complex applications in heterogeneous network environments. The problem of resource management and task allocation has always been one of the main challenges. In this paper, we present an efficient task allocation strategy for distributing tasks onto computing nodes in the underlying heterogeneous networks. The contribution of the proposed technique is to minimize average turnaround time by dispatching tasks to processors with smallest communication ratio. System throughput could be also enhanced by dispersing processor idle time. The proposed technique can be applied to heterogeneous cluster systems as well as computational grid environments, in which the communication costs vary in different clusters. Experimental results show that the proposed scheme outperforms other previous algorithms in terms of throughput and turnaround time.

移动计算环境中基于移动代理的数据管理体系结构

在研究、分析移动计算环境特殊性的基础上,提出了一种基于移动代理的数据管理体系结构,简称DMABMA。该体系结构能把移动计算环境分成两大部分：固定网络部分和无线网络部分,充分利用固定网络部分的高带宽和高可靠性来弥补无线网络部分的断接性、有限带宽和弱可靠性,最终使两者有机结合,形成一个适合移动计算环境需要的体系结构。