A self‐organized load balancing mechanism for cloud computing

The growth in computer and networking technologies over the past decades established cloud computing as a new paradigm in information technology. The cloud computing promises to deliver cost‐effective services by running workloads in a large scale data center consisting of thousands of virtualized servers. The main challenge with a cloud platform is its unpredictable performance. A possible solution to this challenge could be load balancing mechanism that aims to distribute the workload across the servers of the data center effectively. In this paper, we present a distributed and scalable load balancing mechanism for cloud computing using game theory. The mechanism is self‐organized and depends only on the local information for the load balancing. We proved that our mechanism converges and its inefficiency is bounded. Simulation results show that the generated placement of workload on servers provides an efficient, scalable, and reliable load balancing scheme for the cloud data center. Copyright © 2016 John Wiley & Sons, Ltd.     

Elastic management of web server clusters on distributed virtual infrastructures

Cluster‐based solutions are being widely adopted for implementing flexible, scalable, low‐cost and high‐performance web server platforms. One of the main difficulties to implement these platforms is the correct dimensioning of the cluster size, so as to satisfy variable and peak demand periods. In this context, virtualization is being adopted by many organizations as a solution not only to provide service elasticity, but also to consolidate server workloads, and improve server utilization rates. A virtualized web server can be dynamically adapted to the client demands by deploying new virtual nodes when the demand increases, and powering off and consolidating virtual nodes during periods of low demand. Furthermore, the resources from the in‐house infrastructure can be complemented with a cloud provider (cloud bursting), so that peak demand periods can be satisfied by deploying cluster nodes in the external cloud, on an on‐demand basis. In this paper, we analyze the scalability of hybrid virtual infrastructures for two different distributed web server cluster implementations: a simple web cluster serving static files and a multi‐tier web server platform running the CloudStone benchmark. Copyright © 2011 John Wiley & Sons, Ltd.     

云计算中nosql集群自动配置框架的研究与实现

针对云计算中大数据的管理与存储需要手动扩展或释放资源,效率低下、成本高等问题,提出一种云计算环境下nosql数据自动配置管理解决方案,设计并实现有良好的伸缩性、自适应性、可扩展性,支持负载动态变化的nosql自动配置框架,该框架支持所有nosql数据库,广泛集成第三方云平台如亚马逊EC2、openstack、eucalyptus等。在该框架的基础上,实现了Nosman自动配置系统,经运行,该系统能够正常提供大数据的存储与管理,在保证用户SLA的情况下,能够随负载的动态变化自动配置nosql集群资源。     

基于混合云的企业信息平台架构研究

本文基于混合云,探索企业信息平台的架构问题。由于私有云的处理能力有限,在遇到季节性的峰值需求时难以应对;此时,公有云是一种很好的选择,其处理能力远远超出私有云,由于其规模效应,短期而言,成本也低于私有云。然而,将敏感的数据存放到公有云上存在一定的安全隐患,而且,将应用系统部署到公有云上,跨云的负载均衡也存在一定的技术复杂度。本文结合私有云与公有云的优势,给出一种企业信息平台的参考架构,并进行应用系统的负载均衡、数据的安全和混合云互操作性的探讨。     

Cloud‐based parallel solution for estimating statistical significance of megabyte‐scale DNA sequences

Confidence in a pairwise local sequence alignment is a fundamental problem in bioinformatics. For huge DNA sequences, this problem is highly compute‐intensive because it involves evaluating hundreds of local alignments to construct an empirical score distribution. Recent parallel solutions support only kilobyte‐scale sequence sizes and/or are based on sophisticated infrastructures that are not available for most of the research labs. This paper presents an efficient parallel solution for evaluating the statistical significance for a pair of huge DNA sequences using cloud infrastructures. This solution can receive requests from various researchers via web‐portal and allocate resources according to their demand. In this way, the benefits of cloud‐based services can be achieved. The fundamental innovation of this research work is proposing an efficient solution that utilizes both shared and distributed memory architectures via cloud technology to enhance the performance of evaluating the statistical significance for pair of DNA sequences. Therefore, the restriction on the sequence sizes is released to be in megabyte‐scale, which was not supported before for the statistical significance problem. The performance evaluation of the proposed solution was carried out on Microsoft's cloud and compared with the existing parallel solutions. The results show that the processing speed outperforms the recent cluster solutions that target the same problem. In addition, the performance metrics exhibit linear behavior for the addressed number of instances. Copyright © 2012 John Wiley & Sons, Ltd.     

私有云平台服务能力检测方法

针对现阶段使用私有云评测方法缺失所导致搭建私有云供需不匹配的问题,提出了一种自适应、可扩展的私有云系统能力检测方法,可对基础设施即服务（IaaS）云计算系统能力进行评测。首先,通过私有云应用程序接口动态扩展虚拟机数量;然后,通过性能特征模型选取虚拟机所需配置的硬件信息和操作系统类别,并根据用户的需求差异使用不同的负载模型,形成相应的模拟运行环境;最后,使用云计算服务等级协议（SLA）作为测试标准来衡量私有云服务能力。使用所提方法对Openstack开源私有云平台的能力进行了评测,实验结果表明,所提方法可以准确得出私有云服务能力,结果和用户实测一致。该方法负载生成成本更低,测试效率更高。并将该方法与Openstack自带的组件Rally进行对比,拓展性和负载动态模拟有很大的改进。     

MASHED: Security and privacy-aware mutual authentication scheme for heterogeneous and distributed mobile cloud computing services

ABSTRACT

Rapid development in mobile devices and cloud computing technologies has increased the number of mobile services from different vendors on the cloud platform. However, users of these services are facing different security and access control challenges due to the nonexistence of security solutions capable of providing secure access to these services, which are from different vendors, using a single key. An effective security solution for heterogeneous Mobile Cloud Computing (MCC) services should be able to guarantee confidentiality and integrity through single key-based authentication scheme. Meanwhile, a few of the existing authentication schemes for MCC services require different keys to access different services from different vendors on a cloud platform, thus increases complexity and overhead incurred through generation and storage of different keys for different services.

In this paper, an efficient mutual authentication scheme for accessing heterogeneous MCC services is proposed. The proposed scheme combines the user’s voice signature with cryptography operations to evolve efficient mutual authentication scheme devoid of key escrow problem and allows authorized users to use single key to access the heterogeneous MCC services at a reduced cost.     

面向产业园区的智慧云服务平台的分析与研究

以云计算技术为基础,运用面向服务的架构（SOA）理念,从结构层次、开发运营及SaaS化应用三个维度构建了面向产业园区的智慧云服务平台,旨在解决工业化与信息化深度融合过程中智慧产业园区建设存在的信息化服务模式及成本的问题.本文为产业园区的智慧化提供了可借鉴的模式.     

面向云平台中心化集成调度的跨供应商订单分配模型

跨供应商订单分配可以充分发挥云平台优势,有效整合利用各供应商的剩余资源.鉴于此,提出一种云平台主导的集成调度模式,基于该模式和供应商的产能约束、运输约束、订单约束构建面向云平台中心化集成调度的跨供应商订单分配模型,给出基于改进遗传算法的模型求解方法,并通过算例演示所构建模型和算法的有效性.Matlab实验结果表明,订单能够在各供应商间得以科学分配,实现各供应商闲置资源有效利用和成本最优.     

Framework and deployment of a cloud-based advanced planning and scheduling system

Many small and medium-sized manufacturing enterprises (SMEs) have already implemented enterprise resource planning (ERP) and manufacturing execution system (MES) and began to start the journey of cloud manufacturing; however, the high cost of hardware and software investment, implementation, and maintenance usually hinder SMEs from adopting an advanced planning and scheduling (APS) system. This paper aims to develop a cloud-based APS (C-APS) system framework, the service structure, and approach of deploying the C-APS system in a public cloud infrastructure platform and service provider or hybrid cloud platform. The package diagram is proposed for building the C-APS system's virtual factory model to improve modeling efficiency and data stability. The C-APS system is a cloud-based and object-oriented software; its simulation-based scheduling engine can generate the significant production and operations schedule, and has the characteristics of on-demand self-service, quickly expanding and adjusting to the virtual plant model. The C-APS system's application in a leading automotive part assembly company's printed circuit board production scheduling shows that the input planning data model is easy to maintain. The scheduling quality is high; the computing time is short and acceptable for practical application.     

Cloud theory-based simulated annealing algorithm and application

Using the randomness and stable tendency of a Y condition normal cloud generator, a cloud theory-based simulated annealing algorithm (CSA) is originally proposed, whose characteristic is approximately continuous decrease in temperature and implied “Backfire & Re-Annealing”. It fits the annealing process of solid matter in nature much better, overcomes the traditional simulated annealing algorithm (SA)'s disadvantages, which are slow searching speed and being trapped by local minimum easily, then enhances the veracity of final solution and reduces the time cost of the optimization process simultaneously. Theory analysis proves that CSA is convergent and typical function optimization experiments show that CSA is superior to SA in terms of convergence speed, searching ability and robustness. The result of the application using CSA for multiple observers sitting problem (MOST) in visibility-based terrain reasoning (VBTR) also declares the new algorithm's usefulness and effectiveness adequately.     

基于物联网技术的货车载荷实时监测系统设计

针对目前货车载荷监测主要依靠固定式称重的现状,设计了一种基于物联网技术的货车载荷实时监测系统。系统以嵌入式系统和多传感器融合算法为核心,由信号采集,信号处理与传输,云服务器等单元组成。采用主控芯片STM32F105RCT6和电阻式应变片传感器,实现数据采集与数据处理;处理后的信息通过无线通信模块发送至云端,云端服务器利用神经网络技术对所得到的数据进行拟合处理,实现了用户在远程情况下对货车的载重量进行实时的监测与管控。实验测试证明,本系统能够准确,高效地采集到货车载荷量,并且所测货车载荷量的精度在2.75%以内,无线传输效果稳定,并能将载荷数据实时上传至云平台,反馈于用户。本系统综合运用了智能传感器、物联网和云计算等技术,具有实时强、精度高、装配便捷等特点。     

Openstack虚拟化流量平台监控系统

云平台监控系统是有效保障云服务质量的重要环节,本文在Openstack云平台上进行相应的虚拟服务器监控系统设计. 系统结合Openstack的开源性和强大的可扩展性的特点,以Openstack中的网络组件Neutron面对庞大虚拟服务器流量转发时所出现的瓶颈问题为出发点,结合SDN软件定义网络技术及其重要的南向接口协议OpenFlow解决流量瓶颈问题,设计基于Libvirt和sFlow协议的监控系统,负责获取虚拟机群数据流量信息,并且向上层控制模块以及用户反馈最新的平台负载情况,使用控制模块和流表控制流量转发,最终使整个系统平台达到负载均衡.     

LVS资源负载策略应用研究

LVS （Linux Virtual Server）是提高云平台资源利用率的方案之一,但是由于LVS负载均衡算法的权值设置不科学以及在分配连接请求时不能实时地平衡任务,导致云环境中的服务器负载失衡,降低了系统对外提供服务的能力.针对以上问题,本文将模拟退火算法和加权最小连接算法相结合,提出一个基于最佳负载因子的负载均衡策略.并通过实验证明,最佳负载因子策略能够使集群中节点负载更加均衡,极大程度上提高了集群资源利用率.     

An application of interpretive structural modelling to analyse preference structure in multiobjective dynamic environmental-economic systems†

This paper applies ISM (interpretive structural modelling) to analyse preference structure in a multiobjective problem of a dynamic environmental-economic model, and terms this method IPSM (interpretive preference structural modelling). Multi-objectives in the model include minimizing social cost of pollution, and maximizing total consumption in a planning interval. IPSM analyses decision-maker's preference relations (mental model) among many Pareto-optimal solutions of the multiobjective systems under the interactive cooperation between the decision-maker and the model-builder, and extracts the decision-maker's preference structure as a hierarchical directed graph, where the vertices of the graph correspond to the Pareto-optimal solutions and the directed edges correspond to the preference relations. An interesting economic interpretation for a preferred solution is discussed in terms of the economic interpretation of the discrete minimum principle.     

基于Azure的云安全研究

随着云计算的进一步推进和发展,云计算面临的安全问题变得越来越突出,特别是在云计算带来的诸多利益下,如何满足用户在云计算环境下对用户数据的机密性、完整性等相关性能的需求,已成为云计算安全的首要难题。文中以微软Azure平台为基础,从云安全分析人手,针对WindowsAzure云安全进行分析研究。在Azure架构下,探讨Azure云安全解决方案,分别从IaaS、PaaS、SaaS三个角度对微软Azure云平台安全方案给出应对措施,为进一步对微软Azure云平台进行更深层次的测评分析提供技术支持。     

Novel dynamic load balancing algorithm for cloud-based big data analytics

Big data analytics in cloud environments introduces challenges such as real-time load balancing besides security, privacy, and energy efficiency. This paper proposes a novel load balancing algorithm in cloud environments that performs resource allocation and task scheduling efficiently. The proposed load balancer reduces the execution response time in big data applications performed on clouds. Scheduling, in general, is an NP-hard problem. Our proposed algorithm provides solutions to reduce the search area that leads to reduced complexity of the load balancing. We recommend two mathematical optimization models to perform dynamic resource allocation to virtual machines and task scheduling. The provided solution is based on the hill-climbing algorithm to minimize response time. We evaluate the performance of proposed algorithms in terms of response time, turnaround time, throughput metrics, and request distribution with some of the existing algorithms that show significant improvements.

     

大数据技术在火电厂SCR脱硝系统中的应用

针对SCR脱硝系统过量喷氨造成的运行成本升高,催化剂寿命降低,空预器阻塞风险加大等问题,提出应用大数据分析及建模技术解决脱硝系统现存问题的解决方案,主要包括应用大数据挖掘及建模技术,对SCR系统最优喷氨量、催化剂寿命及实时活性、空预器差压及阻塞风险等进行算法设计及智能预测,同时依据所设计算法搭建了对应的大数据智慧环保平台,并对该平台建设方案、系统架构及应用效果进行了说明。通过对平台应用前后SCR脱硝系统的经济效益对比可知,该平台的应用大幅的降低了系统运行成本,提高了企业综合经济效益。作为大数据技术在SCR脱硝领域的有益尝试,该平台的研发实现了“互联网+”在脱硝领域的新突破,为火电脱硝行业的发展指明了新的方向。     

高效且可验证的多授权机构属性基加密方案

移动云计算对于移动应用程序来说是一种革命性的计算模式,其原理是把数据存储及计算能力从移动终端设备转移到资源丰富及计算能力强的云服务器.但是这种转移也引起了一些安全问题,例如,数据的安全存储、细粒度访问控制及用户的匿名性.虽然已有的多授权机构属性基加密云存储数据的访问控制方案,可以实现云存储数据的保密性及细粒度访问控制;但其在加密和解密阶段要花费很大的计算开销,不适合直接应用于电力资源有限的移动设备;另外,虽然可以通过外包解密的方式,减少解密计算的开销,但其通常是把解密外包给不完全可信的第三方,其并不能完全保证解密的正确性.针对以上挑战,本文提出了一种高效的可验证的多授权机构属性基加密方案,该方案不仅可以降低加密解密的计算开销,同时可以验证外包解密的正确性并且保护用户隐私.最后,安全分析和仿真实验表明了方案的安全性和高效性.     

公有云存储系统性能评测方法研究

随着云存储系统的迅速发展和广泛使用,许多企业开发者和个人用户将其应用从传统存储迁移至公有云存储系统,因此,云存储系统性能成为企业开发者和个人用户关注的焦点。由于传统测试难以模拟足够多的用户同时访问云存储系统;测试环境构建复杂,测试时间长,准备测试环境成本高;受网络因素及外界其他因素影响,评测结果不稳定。针对以上所述云存储系统性能评测的重点和难点,提出一种“云测试云”的公有云存储系统性能评测方法,该方法通过在云计算平台动态申请足够数量的实例,对公有云存储系统性能进行评测。首先,构建通用的性能评测框架,可动态伸缩申请实例,自动化部署评测工具及负载,控制并发访问云存储系统,自动释放实例及收集并反馈评测结果;其次,提出多维度的性能评测指标,涵盖不同典型应用、不同云存储接口;最后,提出一种可扩展通用的性能评测模型,该模型可以评测常见典型应用的性能,分析云存储性能影响因素,可适用于任何的公有云存储平台。为了验证该方法的可行性、合理性、通用性和可扩展性,利用所提方法对Amazon S3云存储系统进行性能评测,并使用s3cmd验证评测结果的准确性。实验结果表明,评测结果可以为企业开发者和个人用户提供参考意见。