基于运行时模型的多样化云资源管理方法

多样化的受管资源和不断变化的管理需求,使得云管理面临很大的难度和复杂度.面对一个新的特定的管理需求,管理员往往是在已有管理软件的基础上进行二次开发,通过管理功能的获取和组织来构造新的管理系统.然而,由于缺乏通用的方法,二次开发的难度和复杂度依然很大.为了能够根据管理需求快速定制、集成、扩展已有的管理软件,提出一种基于运行时模型的多样化云资源管理方法.首先,在系统管理接口的基础上构造不同受管资源的运行时模型;其次,通过对不同的运行时模型进行合并,来构造包含所有目标受管资源的组合模型;最后,通过组合模型到用户特定模型的转换,来满足特定的管理需求.在OpenStack与Hyperic两款独立管理软件的基础上,实现了基于运行时模型的虚拟机软、硬件资源统一管理系统,验证了方法的可行性和有效性.     

基于PDCA模型的云资源管理方法研究

随着云计算技术的发展,企业级的云计算平台在数字化浪潮中占据越来越重要的地位。云资源管理是云平台运行的关键环节,要在确保云平台稳定运行的基础上提升云资源利用效率,目前大多数企业级平台已经历大规模建设阶段,在使用阶段暴露出闲置资源多、资源分配不均、缺少管理要求及统一规范等问题,故亟需构建一套科学的云资源管理方法论,实现云资源精益管理。在PDCA模型的基础上,提出了云资源管理方法,拓展管理者对云资源全过程管理的视野,挖掘痛点问题,为企业级云计算平台长效化管理提供参考。     

基于Ceilometer Alarm API的操作设计和实现

随着云计算技术的不断发展,越来越多的企业采用OpenStack来构建私有云或公有云平台.云平台正逐步替代传统服务器,用来承载着企业和用户的IT业务.为了保证云平台的服务质量,本文基于OpenStack的报警功能接口——Ceilometer Alarm API设计和实现了对于云平台虚拟机监控报警功能的交互操作页面.通过使用该功能,用户可以监控虚拟机运行时的性能状态,保证云平台的可靠运行.     

基于移动云计算的党员信息系统的设计与实现

随着在籍党员人数的不断增加,人才交流中心党员管理的难度和复杂度越来越大。为此,在充分调研的基础上将移动云计算与流动党员管理平台相结合,提出设计开发一个党员管理信息系统。经实践证明,系统运行后,流动党员管理的效率大大增强。     

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.     

基于运行时模型的无线传感网管理方法

无线传感网是物联网的核心,主要解决物联网中的信息感知问题,通过散布在特定区域的成千上万的传感器节点,采集环境中各种信息并连接到互联网上.然而,传感设备所采集到的数据是实时的、数量庞大且无良好结构的,要将采集到的数据映射到应用系统的问题域空间,就不得不编写大量的映射代码.为了快速定制和开发物联网系统,提出一种基于运行时模型的无线传感网管理方法：首先,在传感设备管理接口基础上构造运行时模型,并维护运行时模型与采集到信息的数据同步;其次,基于运行时模型,对不同传感设备采集到的数据进行定制、抽取和合并,实现通过组合模型对场景中不同的传感设备进行统一管理;最后,通过模型转换,实现组合模型到应用场景模型的映射,从而能够面向应用场景进行物联网系统的开发.还实现了基于运行时模型的智慧社区原型系统,并对方法的可行性和有效性进行了验证.     

一种基于模型的云计算容错机制开发方法

目前商业云平台和开源云平台种类繁多,如CloudStack,OpenStack,Eucalyptus等,这些云平台提供的管理能力和管理方式存在较大差异,即使在同一个云平台中也存在多种虚拟化方式,如Xen,KVM,VMware等.近年来,随着私有云和混合云的迅速发展,基础设施的异构程度加剧.由于容错机制往往依赖于基础设施的管理能力和管理方式,因此容错机制实例在不同的目标平台上需要分别实现,导致容错机制的开发难度和开发时间显著增加.针对这一问题,提出了一种基于模型的容错机制开发方法,实现容错机制的跨平台性.为了验证容错机制开发方法的有效性和实用性,实现了7种常见的容错机制实例,并在CloudStack和OpenStack开源云平台上进行验证.实验表明,这些容错机制能够有效地实现故障转移,提升容错对象可靠性、可用性等指标;提出的容错机制开发方法能够实现跨平台,并达到90%以上的代码复用率;对云平台管理员以及容错机制开发者的问卷调查结果表明,该方法能够较好地提升容错机制的开发体验和开发效率.     

高性能云工作流状态机的原理与实现

传统工作流程通过设计活动和迁移线等元素来实现流程的基本流转,但随之而来的问题是当流程异常复杂,例如存在多达几十个活动且活动之间需要不断跳转交互的情况下,不仅开发复杂度成倍增加,而且运行时性能也会持续降低.为解决此问题,本文基于有限状态机的原理,结合云计算技术,提出了云工作流状态机,能够充分利用代码逻辑开发的便捷性,简化流程的活动和迁移,最终达到运行时高性能的架构目标.文中详细阐述了工作流状态机的实现原理,运行机制,以及云计算下的状态机服务框架,最后介绍了基于云状态机的业务应用开发方式,并给出容器下的压力测试结果：流程在双节点流转的单步耗时非常短,运行稳定.实践证明,基于容器的云架构在保证可扩展性的同时亦能满足高性能的设计目标.     

基于云平台的Portal集成方法研究与实现

随着开源云平台技术的发展,管理云平台的方式和手段越来越多,如何方便使用云平台下的各种软件成为一个重要的研究课题。通过对当前开源云平台和开源Portal的分析,介绍当前开源Portal集成系统的方法以及主流开源云平台的接口;在此基础上,提出一个应用Portal实现开源云平台下的软件资源整合的通用方法。最后,应用LiferayPortal的资源聚集功能,实现了Hadoop云平台功能在Web端口的集成。该方法不仅为用户提供一个统一的基于Web方式的云平台软件和资源的访问接口,还可以使用户能够统一访问管理云平台下的软件。     

云平台分布式智能感知物联网应用开发

为克服使用传统嵌入式模式开发物联网应用的复杂性,以实现将千差万别的设备使用标准的方式进行连接和访问,能将各种设备按统一的接口和协议进行访问,并构建分布式物联网架构,最终实现物联网与云平台的融合,通过云平台任何终端均可访问物联网的所有设备;研究使用基于Node.js[1]的Zetta[2]框架将所有设备发布为统一的REST API接口,通过Zetta提供的Link机制并使用协议WebSocket实现与云平台上Zetta服务器的实时数据通讯,保持云平台与物联网设备的同步;实现了全新的物联网应用开发模式和架构,构建了一个高效实时同步、开发快捷、维护简便的分布式物联网应用;使用统一的模型化方法和高效Node.js平台实现快速开发基于云平台的物联网应用,克服了传统开发使用C语言开发物联网的弊病。     

A Provenance-based Adaptive Scheduling Heuristic for Parallel Scientific Workflows in Clouds

In the last years, scientific workflows have emerged as a fundamental abstraction for structuring and executing scientific experiments in computational environments. Scientific workflows are becoming increasingly complex and more demanding in terms of computational resources, thus requiring the usage of parallel techniques and high performance computing (HPC) environments. Meanwhile, clouds have emerged as a new paradigm where resources are virtualized and provided on demand. By using clouds, scientists have expanded beyond single parallel computers to hundreds or even thousands of virtual machines. Although the initial focus of clouds was to provide high throughput computing, clouds are already being used to provide an HPC environment where elastic resources can be instantiated on demand during the course of a scientific workflow. However, this model also raises many open, yet important, challenges such as scheduling workflow activities. Scheduling parallel scientific workflows in the cloud is a very complex task since we have to take into account many different criteria and to explore the elasticity characteristic for optimizing workflow execution. In this paper, we introduce an adaptive scheduling heuristic for parallel execution of scientific workflows in the cloud that is based on three criteria: total execution time (makespan), reliability and financial cost. Besides scheduling workflow activities based on a 3-objective cost model, this approach also scales resources up and down according to the restrictions imposed by scientists before workflow execution. This tuning is based on provenance data captured and queried at runtime. We conducted a thorough validation of our approach using a real bioinformatics workflow. The experiments were performed in SciCumulus, a cloud workflow engine for managing scientific workflow execution.     

Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments

A hybrid cloud integrates private clouds and public clouds into one unified environment. For the economy and the efficiency reasons, the hybrid cloud environment should be able to automatically maximize the utilization rate of the private cloud and minimize the cost of the public cloud when users submit their computing jobs to the environment. In this paper, we propose the Adaptive-Scheduling-with-QoS-Satisfaction algorithm, namely AsQ, for the hybrid cloud environment to raise the resource utilization rate of the private cloud and to diminish task response time as much as possible. We exploit runtime estimation and several fast scheduling strategies for near-optimal resource allocation, which results in high resource utilization rate and low execution time in the private cloud. Moreover, the near-optimal allocation in the private cloud can reduce the amount of tasks that need to be executed on the public cloud to satisfy their deadline. For the tasks that have to be dispatched to the public cloud, we choose the minimal cost strategy to reduce the cost of using public clouds based on the characteristics of tasks such as workload size and data size. Therefore, the AsQ can achieve a total optimization regarding cost and deadline constraints. Many experiments have been conducted to evaluate the performance of the proposed AsQ. The results show that the performance of the proposed AsQ is superior to recent similar algorithms in terms of task waiting time, task execution time and task finish time. The results also show that the proposed algorithm achieves a better QoS satisfaction rate than other similar studies.     

面向云计算的粒度自配置日志采集平台

日志数据管理系统是最重要的云服务基础设施之一。重要日志数据缺失将造成相应日志分析与决策的片面性和不准确性。然而日志数据采集能力越强,日志采集的运行期开销就越大,海量日志数据的管理与分析就越耗时,对整个云服务环境的系统性能造成不可忽视的影响。针对如何采集必要的日志数据同时尽可能降低其运行期开销的问题,文章首先提出日志采集粒度的概念,然后设计并编程实现一个面向云计算的粒度自配置日志采集平台。其中,平台构成模块包括:日志采集工具、存储日志采集粒度规则和事实的知识库;基于规则动态增加或关闭相关日志数据采集模块的推理机;相应的图形界面,包括用于添加或修改知识库规则的管理界面和直观查看日志数据的用户界面。最后,初步的案例学习结果表明了平台的有效性。     

Rafhyc: an Architecture for Constructing Resilient Services on Federated Hybrid Clouds

Federated hybrid clouds is a model of service access and delivery to community cloud infrastructures. This model opens an opportunity window to allow the integration of the enhanced science (eScience) with the Cloud paradigm. The eScience is computationally intensive science that is carried out in highly distributed computing infrastructures. Nowadays, the eScience big issue on Cloud Computing is how to leverage on-demand computing in scientific research. This requires innovation at multiple levels, from architectural design to software platforms. This paper characterizes the requirements of a federated hybrid cloud model of Infrastructure as a Service (IaaS) to provide eScience. Additionally, an architecture is defined for constructing Platform as a Service (PaaS) and Software as a Service (SaaS) in a resilient manner over federated resources. This architecture is named Rafhyc (for Resilient Architecture of Federated HYbrid Clouds). This paper also describes a prototype implementation of the Rafhyc architecture, which integrates an interoperable community middleware, named DIRAC, with federated hybrid clouds. In this way DIRAC is providing SaaS for scientific computing purposes, demonstrating that Rafhyc architecture can bring together eScience and federated hybrid clouds.     

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.     

On the performance and power consumption analysis of elastic clouds

Cloud computing is a novel paradigm capable of rationalizing the use of computational resources by means of outsourcing and virtualization. Elasticity is one of the most attractive features of cloud computing. Elastic clouds are able to adapt to workload changes by provisioning and de‐provisioning resources in an autonomic manner, such that at each point in time the available resources match the current demand as closely as possible. However, elasticity adds complexity, which makes quantitative analysis of cloud performance and power consumption difficult. Such analysis is required to evaluate and quantify the cost‐benefit of a strategy portfolio and the quantitative runtime performance and power consumption experienced by cloud‐users. In this study, we present a comprehensive analytical approach to performance and power consumption analysis of elastic clouds. Several metrics are defined and evaluated: expected task completion time, power consumption rate, and task rejection rate under different load conditions, elasticity intensities, and error intensities. To validate the proposed approach, we obtain experimental data through a real‐world cloud and conduct a confidence interval analysis. The analysis results suggest the perfect coverage of theoretical results by corresponding experimental confidence intervals. Copyright © 2016 John Wiley & Sons, Ltd.     

Simplified cloud-oriented virtual machine management with MLN

System administrators are faced with the challenge of making their existing systems power-efficient and scalable. Although cloud computing is offered as a solution to this challenge by many, we argue that having multiple interfaces and cloud providers can result in more complexity than before. This paper addresses cloud computing from a user perspective. We show how complex scenarios, such as an on-demand render farm and scaling web-service, can be achieved utilizing clouds but at the same time keeping the same management interface as for local virtual machines. Further, we demonstrate that by enabling the virtual machine to have its policy locally instead of in the underlying framework, it can move between otherwise incompatible cloud providers and sites in order to achieve its goals more efficiently.