云数据中心分布式监控架构研究与平台设计

对云数据中心大规模资源监控的架构进行研究,并在监控平台中实现了研究的成果。提出一种多线程、分布式监控架构,支持云数据中心在规模增加时,监控平台的水平扩展。设计一种可扩展的适配器框架,实现监控协议或自定义监控接口的动态扩展。设计一种主/被动轮询检测模型,在监控主机工作负载和监控数据准确可靠之间形成平衡。在系统实现中设计了平台总体架构,并重点对监控数据采集处理层进行详细设计和实现。实际应用表明分布式监控架构具有良好的扩展性和适应性,能满足大规模云数据中心资源监控的要求。     

多云平台监控系统的设计与实现

近年来,随着云计算的兴起和逐步发展,云平台已成为各类企业和机构提供服务的主要方式,为保证云平台高效稳定运行,需要对云平台进行实时监控。针对以上需求,基于开源监控工具Ganglia和Ceilometer,设计并实现了一个多云平台监控系统,提供云平台、物理机和虚拟机中内存、CPU和硬盘等资源使用情况的监控能力,同时,通过物理资源管理、监控策略管理、日志管理和用户管理等功能,提升监控系统的可扩展水平和用户友好程度。最终,实现对多云平台全方位可视化的管理和控制。     

基于开源软件的云监控平台设计与实现

最近几年,基于云计算的互联网服务迅速增长,数据中心的基础设施和应用资源不断增加。需要及时、高效的监控和管理资源的可用性,保证云服务的可靠性。通过对云监控的内容和特性的研究,提出一种管理云计算资源的云监控系统架构,重点对分布式架构、逻辑处理模块和云监控接口进行设计,实例中完成对私有云中物理主机、虚拟机、应用服务的批量监控,实现数据的存储和展示。     

广东省“互联网+现代水利”总体框架与关键内容

针对广东省"互联网+现代水利"行动计划,提出由基础设施、数据资源、应用支撑等构成的现代水利的基础框架,针对资源整合与服务共享的重点领域,提出建立云平台服务体系的关键内容,包括计算存储资源动态分配的云平台物理环境、分布式云平台数据资源目录体系、统一架构的云平台应用支撑服务,以此支撑业务应用协同智能、信息资源整合共享、基础设施集约完善的总体目标。     

基于OpenStack和Cloudify的自伸缩云平台体系

现有Web服务在面临高并发请求时,会出现响应时间增加,甚至服务器宕机的问题,为此提出一种基于云计算的自伸缩分布式Web系统架构。该架构构建在OpenStack基础设施即服务(IaaS)的平台之上,结合Cloudify平台即服务(PaaS)的平台,实现了自伸缩云应用平台(ECAP);并提出以虚拟机资源模板为标度值来构建模糊矩阵,实现模糊层次分析调度算法。最后在云平台上上传测试应用,并使用压力测试工具对平台进行了测试分析,结果表明所构建平台比普通应用服务器在应用的平均响应时间和负荷性能上表现更好。     

基于OpenStack资源监控系统

资源监控是提高云平台可靠性的重要手段. 本文结合OpenStack云平台的特点,设计并实现了一个全面、智能、高效的资源监控系统,完成了资源监控系统整体架构的设计以及各模块的功能划分,并给出了实例监控方式、数据存储模型等具体的实现方法. 最后,通过测试结果说明资源监控系统的有效性.     

多单位共用园区网管理模式的思考

对园区内多单位共用网络的管理模式进行探讨,提出了构建面向多租户架构的资源管理框架,提供基础设施、系统平台、应用平台等不同类型的服务资源,并建立相应的共享和隔离机制的管理思路.     

一种轻量级的监控架构设计

随着安全要求的增强以及不断发展的平台资源监控需要,业界比较成熟的监控架构不再适应发展。提出一种轻量级的两层监控系统架构,通过Expect技术灵活实现轻量级、可扩展的平台资源监控,满足发展需要。通过在某公司依据该架构设计实现的监控系统的上线使用,有效证明了该架构的可行。     

遵义市智慧规划大数据云平台设计与实现

在国家政策和规划变革的驱动下,当前规划信息化系统的业务支撑包容性、数据资源复杂度及服务对象需求都在不断增长,原有平台在IT架构支撑、内外流程串联、数据增值空间和政务服务模式等方面的潜力已经见底,不能应对当前政务改革和服务创新提出的要求。城市要现代化,基础设施必须现代化,这就要求加快规划建设智慧城市,大力建设各类“专业云”和“特色云”,推动一体化的大数据云平台建设,提高城市精细化、智能化、信息化水平。本文基于成熟的面向服务的架构（SOA）体系,采用主流的GIS技术和数据库技术,提出了平台建设目标、设计思路和总体框架,最终实现了一套智慧规划大数据云平台。     

SIG中的资源监控系统

空间信息网格（SIG）是一个广域分布的信息基础设施,发现与共享闲置资源是其主要功能之一。为了实现这一目标,必须实现资源监控系统来聚集和分发资源的状态信息。介绍了在SIG中实现的资源监控系统——SIGRMS。该系统遵循GMA架构,实现了局部监控服务（LMS）和全局监控服务（GMS）。系统采用模块化设计,具有灵活的可配置性,可扩展性和高可用性。详细介绍了SIGRMS模块化的体系结构、灵活可扩展的信息模板和推拉式的通信方式,并描述了它的实现,最后总结了SIGRMS的特点。     

基于运行时模型的混合云管理方法

随着云计算技术的普及,涌现出众多不同用途、不同类型的云计算平台.为了满足遗产系统整合和动态资源扩展等需求,常常需要构造混合云来统一管理不同云平台中的计算和存储资源.然而,不同云平台的管理接口和管理机制存在差异,使得开发混合云管理系统难度大、复杂度高.本文提出一种基于运行时模型的混合云管理方法：首先,在云平台管理接口基础上,构造单一云平台的运行时模型;其次,根据云平台领域知识,提出一种云平台软件体系结构的统一模型;最后,通过模型转换,实现云平台统一模型到运行时模型的映射.于是,管理程序可以建立在云平台统一模型的基础上,降低了混合云管理系统开发的难度和复杂度.本文还实现了基于运行时模型的CloudStack和亚马逊EC2混合云管理系统,并对方法的可行性和有效性进行了验证.     

Providing security and fault tolerance in P2P connections between clouds for mHealth services

The mobile health (mHealth) and electronic health (eHealth) systems are useful to maintain a correct administration of health information and services. However, it is mandatory to ensure a secure data transmission and in case of a node failure, the system should not fall down. This fact is important because several vital systems could depend on this infrastructure. On the other hand, a cloud does not have infinite computational and storage resources in its infrastructure or would not provide all type of services. For this reason, it is important to establish an interrelation between clouds using communication protocols in order to provide scalability, efficiency, higher service availability and flexibility which allow the use of services, computing and storage resources of other clouds. In this paper, we propose the architecture and its secure protocol that allows exchanging information, data, services, computing and storage resources between all interconnected mHealth clouds. The system is based on a hierarchic architecture of two layers composed by nodes with different roles. The routing algorithm used to establish the connectivity between the nodes is the shortest path first (SPF), but it can be easily changed by any other one. Our architecture is highly scalable and allows adding new nodes and mHealth clouds easily, while it tries to maintain the load of the cloud balanced. Our protocol design includes node discovery, authentication and fault tolerance. We show the protocol operation and the secure system design. Finally we provide the performance results in a controlled test bench.     

Combined Cloud: A Mixture of Voluntary Cloud and Reserved Instance Marketplace

Voluntary cloud is a new paradigm of cloud computing. It provides an alternative selection along with some well-provisioned clouds. However, for the uncertain time span that participants share their computing resources in voluntary cloud, there are some challenging issues, i.e., fluctuation, under-capacity and low-benefit. In this paper, an architecture is first proposed based on Bittorrent protocol. In this architecture, resources could be reserved or requested from Reserved Instance Marketplace and could be accessed with a lower price in a short circle. Actually, these resources could replenish the inadequate resource pool and relieve the fluctuation and under-capacity issue in voluntary cloud. Then, the fault rate of each node is used to evaluate the uncertainty of its sharing time. By leveraging a linear prediction model, it is enabled by a distribution function which is used for evaluating the computing capacity of the system. Moreover, the cost optimization problem is investigated and a computational method is presented to solve the low-benefit issue in voluntary cloud. At last, the system performance is validated by two sets of simulations. And the experimental results show the effectiveness of our computational method for resource reservation optimization.     

Distributed,application-level monitoring for heterogeneous clouds using stream processing

As utility computing is widely deployed, organizations and researchers are turning to the next generation of cloud systems: federating public clouds, integrating private and public clouds, and merging resources at all levels (IaaS, PaaS, SaaS). Adaptive systems can help address the challenge of managing this heterogeneous collection of resources. While services and libraries exist for basic management tasks that enable implementing decisions made by the manager, monitoring is an open challenge. We define a set of requirements for aggregating monitoring data from a heterogeneous collections of resources, sufficient to support adaptive systems. We present and implement an architecture using stream processing to provide near-realtime, cross-boundary, distributed, scalable, fault-tolerant monitoring. A case study illustrates the value of collecting and aggregating metrics from disparate sources. A set of experiments shows the feasibility of our prototype with regard to latency, overhead, and cost effectiveness.     

A cost-aware auto-scaling approach using the workload prediction in service clouds

Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand computing power and storage capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a novel service cloud architecture is presented, and a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The auto-scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.     

Cost-Aware and SLO-Fulfilling Software as a Service

Virtualization technology makes data centers more dynamic and easier to administrate. Today, cloud providers offer customers access to complex applications running on virtualized hardware. Nevertheless, big virtualized data centers become stochastic environments and the simplification on the user side leads to many challenges for the provider. He has to find cost-efficient configurations and has to deal with dynamic environments to ensure service level objectives (SLOs). We introduce a software solution that reduces the degree of human intervention to manage clouds. It is designed as a multi-agent system (MAS) and placed on top of the Infrastructure as a Service (IaaS) layer. Worker agents allocate resources, configure applications, check the feasibility of requests, and generate cost estimates. They are equipped with application specific knowledge allowing it to estimate the type and number of necessary resources. During runtime, a worker agent monitors the job and adapts its resources to ensure the specified quality of service—even in noisy clouds where the job instances are influenced by other jobs. They interact with a scheduler agent, which takes care of limited resources and does a cost-aware scheduling by assigning jobs to times with low costs. The whole architecture is self-optimizing and able to use public or private clouds. Building a private cloud needs to face the challenge to find a mapping of virtual machines (VMs) to hosts. We present a rule-based mapping algorithm for VMs. It offers an interface where policies can be defined and combined in a generic way. The algorithm performs the initial mapping at request time as well as a remapping during runtime. It deals with policy and infrastructure changes. An energy-aware scheduler and the availability of cheap resources provided by a spot market are analyzed. We evaluated our approach by building up an SaaS stack, which assigns resources in consideration of an energy function and that ensures SLOs of two different applications, a brokerage system and a high-performance computing software. Experiments were done on a real cloud system and by simulations.     

A fault‐tolerant dynamic scheduling method on hierarchical mobile edge cloud computing

Mobile edge cloud computing has been a promising computing paradigm, where mobile users could offload their application workloads to low‐latency local edge cloud resources. However, compared with remote public cloud resources, conventional local edge cloud resources are limited in computation capacity, especially when serve large number of mobile applications. To deal with this problem, we present a hierarchical edge cloud architecture to integrate the local edge clouds and public clouds so as to improve the performance and scalability of scheduling problem for mobile applications. Besides, to achieve a trade‐off between the cost and system delay, a fault‐tolerant dynamic resource scheduling method is proposed to address the scheduling problem in mobile edge cloud computing. The optimization problem could be formulated to minimize the application cost with the user‐defined deadline satisfied. Specifically, firstly, a game‐theoretic scheduling mechanism is adopted for resource provisioning and scheduling for multiprovider mobile applications. Then, a mobility‐aware dynamic scheduling strategy is presented to update the scheduling with the consideration of mobility of mobile users. Moreover, a failure recovery mechanism is proposed to deal with the uncertainties during the execution of mobile applications. Finally, experiments are designed and conducted to validate the effectiveness of our proposal. The experimental results show that our method could achieve a trade‐off between the cost and system delay.     

云计算集群服务器系统监控方法的研究

随着云计算技术越来越多地应用到信息产业的各个领域,云计算环境下集群服务器系统的监控与管理的需求越来越大。云计算下的集群服务器系统主要是通过一系列基于分布式架构的服务器集群组成,其下的服务器数量可能多达上万台。要管理好数量如此大的云计算集群服务器系统,保证其高性能运行,必然需要一套有效的云计算集群监控系统对其进行监测与调控。但是,传统的集群监测系统存在一些不足与弊端。本文对于云计算集群系统的高性能监测调度方案进行了研究,从云监控系统的架构、数据采集、负载均衡调度方面进行了探讨,构建了一个保证云计算集群系统高性能运营的云系统方案。     

Media Cloud: When Media Revolution Meets Rise of Cloud Computing

Media content has become the major traffic of Internet and will keep on increasing rapidly. Various innovative media applications, services, devices have emerged and people tend to consume more media contents. We are meeting a media revolution. But to maintain the sustainability of the tremendous media consumption, it requires great capability of media processing, which demands huge amount of computing resources. Meanwhile cloud computing has emerged as a prosperous technology and the cloud computing platform has become a fundamental facility providing various services, great computing power, massive storage and bandwidth with modest cost. The integration of cloud computing and media processing is therefore a natural choice for both of them, and hence comes forth the media cloud. In this paper we make a comprehensive overview on the recent media cloud research work. We first discuss the challenges of the media cloud, and then summarize its architecture, the processing, and its storage, delivery and resource management mechanisms. As the result, we propose a new architecture for the media cloud. At the end of this paper, we make suggestions on building media clouds and propose several future research topics as the conclusion.