EMUSIM: an integrated emulation and simulation environment for modeling,evaluation, and validation of performance of Cloud computing applications

Cloud computing allows the deployment and delivery of application services for users worldwide. Software as a Service providers with limited upfront budget can take advantage of Cloud computing and lease the required capacity in a pay‐as‐you‐go basis, which also enables flexible and dynamic resource allocation according to service demand. One key challenge potential Cloud customers have before renting resources is to know how their services will behave in a set of resources and the costs involved when growing and shrinking their resource pool. Most of the studies in this area rely on simulation‐based experiments, which consider simplified modeling of applications and computing environment. In order to better predict service's behavior on Cloud platforms, we developed an integrated architecture that is based on both simulation and emulation. The proposed architecture, named EMUSIM, automatically extracts information from application behavior via emulation and then uses this information to generate the corresponding simulation model. We performed experiments using an image processing application as a case study and found that EMUSIM was able to accurately model such application via emulation and use the model to supply information about its potential performance in a Cloud provider. We also discuss our experience using EMUSIM for deploying applications in a real public Cloud provider. EMUSIM is based on an open source software stack and therefore it can be extended for analysis behavior of several other applications. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

面向云计算的网络化平台研究与实现

云计算提供三种类型的服务:基础设施即服务、平台即服务和软件即服务。很多云实例都采用高性能计算结点构建基础设施,而高性能计算机的传统使用方式制约了云平台型服务的发展。本文设计并实现了基于高性能计算机的面向云计算的网络化平台NPCC,这是尝试解决高性能计算环境支持提供云平台型服务存在问题的一种探索性研究。NPCC采用了高性能虚拟域HPVZ技术和多目标协同的并行工作负载调度策略等,改变了传统高性能计算机的共享使用方式,为用户提供了具有易用性、通用性、安全性、可定制化和图形化的面向云计算的网络化平台环境。     

云基础设施下的体系结构、挑战与机遇

在工业界和学术界的大力推动下,云计算作为一种新的服务模式,大致可分为将软件作为服务(Software as a service),将平台作为服务(Platform as a service),和将基础设施作为服务(Infrastructure as a Service).现有的绝大部分关于云计算的研究和讨论都集中在前两种服务.本文试图探讨云基础设施的体系结构及其面临的挑战和机遇.从冯.诺伊曼体系结构开始,计算机系统结构的研究基本上就可简单归类于三个问题:计算、存储与传输,三者相互影响.我们认为云基础设施也不例外.本文探讨了云计算的特点和优势,并从云体系结构的角度,探讨了云基础设施下的云计算、云存储和云传输所面临的挑战及其带来的可能的各种技术革命.     

A new cloud computing architecture for music composition

This paper presents an original cloud computing architecture for music composition. In this model, music applications are built by making several computer music services work together. Component services are provided by a dedicated layer in the cloud architecture called computer music as a Service (CMaaS). The specialized music services can be integrated into different applications at the same time. These music services provided by the CMaaS layer are implemented in the form of platform images based on templates at the Platform as a Service layer. The images are ready to be loaded into the virtualized infrastructure on demand. As examples of implementation over the proposed cloud architecture, two powerful applications for computer music composition are presented: “Diatonic Composer”, an interactive composer of scores with high-abstraction music elements, and “Csound Meets the Cloud”, an assisted algorithmic composer focused on sound synthesis. The composition model, the involved music services and the web application are described for the above mentioned applications. The proposed architecture, the implemented services and the provided application examples constitute a decided step towards distributed music computation.     

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。     

云计算和物联网在智慧校园系统构建中的应用

智慧校园是信息技术发展过程中出现的新理念,是云计算、物联网以及其它技术相融合的具体实践、是学校培养人才、提高管理与优化服务的创新。云计算是利用虚拟化技术对各种资源进行深度集成整合,提供超级计算和存储能力,它具有三种服务形式：基础设施即服务（IaaS）、平台即服务（PaaS）、软件即服务（SaaS）。物联网技术是传感网、因特网与移动通信网三网高效融合的产物,核心是物联感知系统,它划分为感知层、网络层和应用层。基于云计算和物联网技术的智慧校园架构由统一门户系统、服务支持平台、数据信息融合平台、网络融合基础平台以及信息标准体系和安全维护体系构成。     

基于物联网云平台的壁挂炉远程控制系统

详细描述了基于Ayla云平台的远程壁挂炉控制系统设计方案与具体实现, 通过Android手机客户端, 接入Ayla物联网云平台, 实现对家中壁挂炉的远程控制. 壁挂炉与Ayla云平台连接的中间设备, 采用以WM-N-BM-09A片上WiFi芯片和STM32F100BV主控芯片为核心的Ayla嵌入式模块进行开发, 利用WiFi通过无线路由与Ayla云平台建立通信通道, 壁挂炉的运行状态信息、报警数据, 通过Ayla云平台的主动推送机制, 实时推送至手机客户端. 测试结果表明, 基于Ayla物联网云平台的远程控制系统方案, 成本低, 易于实现. 此方案可以被应用于其它远程智能控制系统, 为智能设备的远程控制提供了一种低成本、高可靠的解决方案.     

Intelligent heart disease prediction in cloud environment through ensembling

Cloud computing is the delivery of on‐demand computing resources. Cloud computing has numerous applications in fields of education, social networking, and medicine. But the benefit of cloud for medical purposes is seamless, particularly because of the enormous data generated by the health care industry. This colossal data can be managed through big data analytics, and hidden patterns can be extracted using machine learning procedures. In particular, the latest issue in the medical domain is the prediction of heart diseases, which can be resolved through culmination of machine learning and cloud computing. Hence, an attempt has been made to propose an intelligent decision support model that can aid medical experts in predicting heart disease based on the historical data of patients. Various machine learning algorithms have been implemented on the heart disease dataset to predict accuracy for heart disease. Naïve Bayes has been selected as an effective model because it provides the highest accuracy of 86.42% followed by AdaBoost and boosted tree. Further, these 3 models are being ensembled, which has increased the overall accuracy to 87.91%. The experimental results have also been evaluated using 10,082 instances that clearly validate the maximum accuracy through ensembling and minimum execution time in cloud environment.     

Robot Cloud: Bridging the power of robotics and cloud computing

Cloud computing is shaping the cyber world and evolves as a key computing and service platform for sharing resources including platforms, software applications and everything in the form of services. This is known “X as a Service”. Although it brings our age unparalleled computing ability and economic benefits, the application of cloud computing is still limited currently in the cyberspace due to the cloud services can only reside in cloud instead of our daily life environment. In fact, there are still a plethora of physical position based on-site service demands that cloud computing could help little due to the “cyber limitation”. In this paper, we aim to integrate the cyber world and the physical world by bringing up the idea of “Robot Cloud” to bridge the power of robotics and cloud computing. To make it possible, we design a novel Robot Cloud stack to support our idea and adopt the service-oriented architecture (SOA) to make the functional modules in the Robot Cloud more flexible, extensible and reusable. Then we develop a prototype of Robot Cloud using the popular Google App Engine to demonstrate our design method. Finally, we conduct the simulation experiments with a “robot show” application scenario to evaluate our scheduling policy and identify the effect of different request distributions and robot center solutions.     

分布式云的研究进展综述

云计算作为全新的计算模式,将数据中心的资源包括计算、存储等基础设施资源通过虚拟化技术以服务的形式交付给用户,使得用户可以通过互联网按需访问云内计算资源来运行应用.为面向用户提供更好的服务,分布式云跨区域联合多个云站点,创建巨大的资源池,同时利用地理分布优势改善服务质量.近年来分布式云的研究逐渐成为学术界和工业界的热点.文中围绕分布式云系统中研究的基本问题,介绍了国际国内的研究现状,包括分布式云系统的架构设计、资源调度与性能优化策略和云安全方案等,并展望分布式云的发展趋势.     

基于云计算的工商办公自动化系统的设计与实现

办公自动化系统是为适应现代无纸化及网络化办公的趋势,更好地服务于现代办公操作,基于B/S模式而开发的一套应用于工商系统的办公自动化系统.系统在设计与实现上基于云服务模式,将系统构架分为基础设施层(IaaS)、系统平台层(PaaS)、应用服务层(SaaS).基于云服务平台的设计,能够提升系统的可靠性和数据存储安全性,实现硬件资源共享和动态调整,实现应用弹性部署,为各机关提供个性化定制服务,降低运维成本、实现节能减排,并提升技术的先进性,并奠定业务的可扩展基础.     

国内云计算研究现状与未来

云计算一直是学术界和企业界研究的热点,文章基于三大中文数据库提供的数据,从云计算研究现状以及基础设施即服务(IaaS)、平台即服务(PaaS)、软件即服务(SaaS)三方面研究现状对国内云计算的具体研究状况作出了统计、比较与分析,并对未来的发展做了具体的展望。希望通过一系列的研究与总结,对国内云计算的发展研究起到积极的推动作用。     

INDIGO-DataCloud: a Platform to Facilitate Seamless Access to E-Infrastructures

This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications.     

基于多算力融合的数字孪生黄河黄河云设计研究

为解决数字孪生黄河建设过程中出现的算力基础设施能力不足,数据存储、处理、服务效率不高,资源服务模式不够灵活等问题,立足数字孪生黄河建设对算力基础设施的实际需求,结合水利部出台的相关技术规范,提出多算力融合黄河云重构搭建方案。开展对多元算力、多模态存储模式等关键技术的综合分析,提出多算力融合黄河云的总体框架、部署架构、资源池及资源管理设计。多算力融合黄河云针对模型计算、数据底板、智能应用、大数据处理与分析等不同场景,建设虚拟化、高性能、裸金属等算力资源,根据数据类型、特点,以及数据量匹配集中和分布式存储资源,提出适应黄河水利委员会组织架构的云管理模式,可为数字孪生黄河建设提供高效算力底座。     

From cloud computing to cloud manufacturing

Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing—the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.     

Cloud‐based Real‐time Outsourcing Localization for a Ground Mobile Robot in Large‐scale Outdoor Environments

Cloud robotics is the application of cloud computing concepts to robotic systems. It utilizes modern cloud computing infrastructure to distribute computing resources and datasets. Cloud‐based real‐time outsourcing localization architecture is proposed in this paper to allow a ground mobile robot to identify its location relative to a road network map and reference images in the cloud. An update of the road network map is executed in the cloud, as is the extraction of the robot‐terrain inclination (RTI) model as well as reference image matching. A particle filter with a network‐delay‐compensation localization algorithm is executed on the mobile robot based on the local RTI model and the recognized location both of which are sent from the cloud. The proposed methods are tested in different challenging outdoor scenarios with a ground mobile robot equipped with minimal onboard hardware, where the longest trajectory was 13.1 km. Experimental results show that this method could be applicable to large‐scale outdoor environments for autonomous robots in real time.     

State-of-the-art research study for green cloud computing

Although cloud computing has rapidly emerged as a widely accepted computing paradigm, the research on cloud computing is still at an early stage. Cloud computing suffers from different challenging issues related to security, software frameworks, quality of service, standardization, and power consumption. Efficient energy management is one of the most challenging research issues. The core services in cloud computing system are the SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service). In this paper, we study state-of-the-art techniques and research related to power saving in the IaaS of a cloud computing system, which consumes a huge part of total energy in a cloud computing system. At the end, some feasible solutions for building green cloud computing are proposed. Our aim is to provide a better understanding of the design challenges of energy management in the IaaS of a cloud computing system.     

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high‐performance applications, such as local clusters, high‐performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads; hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications.     

云计算虚拟化技术的发展与趋势

云计算是一种融合了多项计算机技术的以数据和处理能力为中心的密集型计算模式,其中以虚拟化、分布式数据存储、分布式并发编程模型、大规模数据管理和分布式资源管理技术最为关键。经过十多年的发展,云计算技术已经从发展培育期步入快速成长期,越来越多的企业已经开始使用云计算服务。与此同时,云计算的核心技术也在发生着巨大的变化,新一代的技术正在改进甚至取代前一代技术。容器虚拟化技术以其轻便、灵活和快速部署等特性对传统的基于虚拟机的虚拟化技术带来了颠覆性的挑战,正在改变着基础设施即服务（IaaS）平台和平台即服务（PaaS）平台的架构和实现。对容器虚拟化技术进行深入介绍,并通过分析和比较阐述容器虚拟化技术和虚拟机虚拟化技术各自的优势、适应场景和亟待解决的问题,然后对云计算虚拟化技术的下一步研究方向和发展趋势进行展望。