一种成本相关的云提供商数据可靠性保证算法

数据可靠性保证和容灾成本控制对云提供商而言是一个相互矛盾的问题.在分析已有数据保障机制的基础上,设计了一个基于多个云平台的分布式富云容灾模型,利用富云容灾系统,私有云提供商可以借用其他云平台的虚拟资源对自身数据进行冗余备份.为了减少数据传输的响应时间,富云容灾模型通过设置多个地理位置隔离的富云代理实现云平台用户任务的分配,减少私有云平台的工作负荷.针对富云容灾系统的成本优化和数据可靠性保证问题,提出了一种成本相关的云计算服务数据可靠性保证算法CAHRPA.该算法根据数据传输带宽和容灾费用在多个云平台中动态选择数据副本的存放位置,从而以一种成本优化的方式为云提供商提供数据容灾方案.实验结果表明,CAHRPA 能够在保证数据可靠性的同时,实现一种成本更低的数据容灾策略.     

一种税务行业数据容灾系统模型设计

介绍了在大集中环境下税务行业面临的各种数据灾难,结合税务行业的应用特点提出税务行业数据容灾系统设计目标。通过使用数据异地磁盘镜像、数据克隆和数据恢复等技术,规划出数据容灾系统模型,并描述数据容灾系统的组成部分和基础运行环境,重点介绍了数据容灾系统的三个组成部分:数据同步子系统、数据备份子系统和数据恢复子系统。     

一种保证服务连续性的容灾系统的设计和实现

设计并实现了一种能够保证服务连续性的容灾系统。系统在设备驱动层使用实时监控写操作的方法,通过Internet将数据备份到多个远程容灾中心;采用多种数据恢复策略,实现数据的快速恢复;提供了服务自动切换机制,保证系统能够对外提供不间断服务。此外,该系统支持多种操作系统和数据库,并提供了基于Web的配置管理方式。     

一种应用级容灾系统的设计与实现

该文设计并实现了一种应用级容灾系统。该系统采用模块化的设计方法,通过多策略的数据备份和数据恢复技术,保护了数据的完整性和高可用性;使用数据复制的方法确保了主备应用系统之间的数据一致性;在灾难发生时,通过服务切换的方法使得备用应用系统能迅速地接替主系统继续提供服务,保持了应用的业务连续性,实现了应用级容灾。     

面向大量用户的远程备份容灾系统

为应对面向大量用户的数据容灾系统的需求,设计并实现了一个面向大量用户的数据备份容灾系统。在远程容灾服务器建立一个与磁盘分区数据一致的备份磁盘分区,对磁盘分区的数据进行实时监控,将监控到的数据变化通过网络实时传递到远程容灾服务器,并更新磁盘分区,采用epoll与哈希表等技术处理来自多个生成服务器的请求,能够同时为大量用户提供备份服务。当灾难发生时,系统将远程容灾服务器的备份数据恢复到磁盘分区,从而降低数据丢失所带来的损失。     

新农合信息系统数据备份与容灾设计

探讨了如何有效增强新农合信息系统数据的安全性,确保业务系统的连续性。利用数据备份与数据容灾技术,并以"整体规划、分步实施、逐年投入"的建设思路来构建新农合信息系统数据备份与容灾系统。详述了新农合信息系统数据备份与容灾系统的三个建设阶段内容,即本地数据备份系统建设、异地数据级容灾建设、应用级容灾建设,并分阶段实施,以确保建成一个有效的、全面的数据备份与容灾系统。     

基于遗传算法的医院Oracle数据库容灾备份方法

常规的医院Oracle数据库容灾备份方法数据恢复时间过长,因此设计一种全新的医院Oracle数据库容灾备份方法。先以成本和时间有效的容灾系统模型体系(Cost and Time Effective Disaster Recovery System,ETDRS)为基础,结合遗传算法选取了有效的Oracle容灾备份编码例子,再根据智能合约构建了有效的数据库容灾备份平台,从而实现了Oracle数据库容灾备份。实验结果表明,设计方法在不同数量数据块下的数据恢复时间均较短,备份效果较好,有一定的应用价值。     

远程容灾系统的性能测试

针对远程容灾系统结构多样、缺乏适用于测试的技术性能指标和不易进行性能测试的现状,设计一种性能测试方法,通过I/O吞吐量、CPU利用率、响应时间和数据备份时间等指标反映容灾系统的性能。利用该方法对远程异步容灾系统测试时,测试数据显示容灾系统对应用服务器响应时间的影响为零,符合异步容灾系统的技术特点,证明该方法适用于容灾系统性能测试。     

基于Fibre Chanel组播的远程数据备份体系结构

数据备份是容灾的基础.本文提出了一种基于FC组播服务的远程数据备份方法,对其工作原理和所涉及到的粗波分复用和流量控制等关键技术进行了研究,对基于FC组播的数据备份系统的性能和所属的容灾层次等内容进行了分析.     

采用位表的块级持续数据容灾系统研究

提出了一种在块级持续数据容灾系统中建立备份位表和恢复位表的方法,有效消除数据恢复过程中冗余读写操作,提高了数据恢复的效率。实验证明,采用位表的块级持续数据容灾系统可以快速地将被保护数据卷恢复到之前任意时刻的正确状态。     

A cost‐efficient resource provisioning algorithm for DHT‐based cloud storage systems

Personal cloud storage provides users with convenient data access services. Service providers build distributed storage systems by utilizing cloud resources with distributed hash table (DHT), so as to enhance system scalability. Efficient resource provisioning could not only guarantee service performance, but help providers to save cost. However, the interactions among servers in a DHT‐based cloud storage system depend on the routing process, which makes its execution logic more complicated than traditional multi‐tier applications. In addition, production data centers often comprise heterogeneous machines with different capacities. Few studies have fully considered the heterogeneity of cloud resources, which brings new challenges to resource provisioning. To address these challenges, this paper presents a novel resource provisioning model for service providers. The model utilizes queuing network for analysis of both service performance and cost estimation. Then, the problem is defined as a cost optimization with performance constraints. We propose a cost‐efficient algorithm to decompose the original problem into a sub‐optimization one. Furthermore, we implement a prototype system on top of an infrastructure platform built with OpenStack. It has been deployed in our campus network. Based on real‐world traces collected from our system and Dropbox, we validate the efficiency of our proposed algorithms by extensive experiments. Copyright © 2016 John Wiley & Sons, Ltd.     

Cost optimal scheduling in IaaS for dependent workload with particle swarm optimization

Optimizing cloud provisioning for scientific workflow applications is a challenging problem, since the workflows generally contain dependency between tasks and require specific deadlines. Usually, cloud providers offer many options to the consumers. These options include the number of virtual machines, the type of each virtual machine and the purchasing method for each machine. Currently, cloud provisioning cost optimization is an active research topic. Most of this literature is concerned with task scheduling, cloud option selection, and cloud option selection for scientific workflow applications. However, research that attempts to find solutions which cover both cloud option selection and workflow task scheduling is very limited. In this paper, we focus on optimizing the cost of purchasing infrastructure-as-a-service cloud capabilities to achieve scientific work flow execution within the specific deadlines. The proposed system considers the number of purchased instances, instance types, purchasing options, and task scheduling as constraints in an optimization process. Particle swarm optimization augmented with a variable neighborhood search technique is used to find the optimal solution. Our approach finds the configurations of purchasing options with the optimum budget for a specified workflow application based on the required performance. The solutions from the proposed system show promising performance from the perspectives of the total cost and fitness convergence when compared with other state-of-the-art algorithms.     

基于云存储的块级连续数据保护系统

提出了基于云存储的块级连续数据保护系统MYCDP。MYCDP利用灵活且性价比高的云资源存储业务数据,并采用全局数据去重技术压缩数据量,以取得更低的备份成本。使用针对性设计的版本索引结构和本地缓存机制优化低带宽、高延迟云环境下的数据恢复速度。给出了系统详细结构设计与具体流程描述,通过原型系统与其他方案系统的对比测试,验证了MYCDP能够取得比同样基于云存储的传统delta编码方案更低的备份成本和更快的恢复速度,且在多数实际恢复场景中具有与传统基于本地存储资源的方案相同甚至更好的数据恢复性能。     

基于改进多目标布谷鸟搜索的资源调度算法

针对IaaS(Infrastructure as a Service)云计算中资源调度的多目标优化问题,提出一种基于改进多目标布谷鸟搜索的资源调度算法。在多目标布谷鸟搜索算法的基础上,通过改进随机游走策略和丢弃概率策略提高了算法的局部搜索能力和收敛速度。以最大限度地减少完成时间和成本为主要目标,将任务分配特定的VM(Virtual Manufacturing)满足云用户对云提供商的资源利用的需求,从而减少延迟,提高资源利用率和服务质量。实验结果表明,该算法可以有效地解决IaaS云计算环境中资源调度的多目标问题,与其他算法相比,具有一定的优势。     

T—YUN：云提供商可信性审计与验证

云计算被广泛认为是信息技术发展的必然趋势。然而,由于在云计算模式下用户失去了对托管在云端的数据和应用的直接控制能力,产生了云服务的可信性问题,严重影响了云计算与云服务的推广。文章设计实现了一种新的通过可信第三方TTP（TrustedTMrdParty）对云提供商可信性进行审计和验证的模型。为了防止TTP成为单点瓶颈或单点故障,通过云计算技术构建TTP云验证平台实现了原型系统,并对其进行定量测试、分析和评价。实验结果表明,T—YUN在有效验证可信性的同时没有引入过多的额外代价。     

云中截止时间动态分配的工作流调度成本优化算法

现如今,如何在满足截止时间约束的前提下降低工作流的执行成本,是云中工作流调度的主要问题之一。三步列表调度算法可以有效解决这一问题。但该算法在截止时间分配阶段只能形成静态的子截止时间。为方便用户部署工作流任务,云服务商为用户提供了的三种实例类型,其中竞价实例具有非常大的价格优势。为解决上述问题,提出了截止时间动态分配的工作流调度成本优化算法(S-DTDA)。该算法利用粒子群算法对截止时间进行动态分配,弥补了三步列表调度算法的缺陷。在虚拟机选择阶段,该算法在候选资源中增加了竞价实例,大大降低了执行成本。实验结果表明,相较于其他经典算法,该算法在实验成功率和执行成本上具有明显优势。综上所述,S-DTDA算法可以有效解决工作流调度中截止时间约束的成本优化问题。     

Analysis of a cloud migration framework for offline risk assessment of cloud service providers

Generic notions of security on cloud platforms make clients apprehensive about fully migrating their applications on these platforms. The challenge lies in the capability of personalizing the security assessments of different cloud service providers from the perspective of the security requirements of the client applications to be hosted on them. This challenge was addressed by the previously proposed offline risk assessment framework for cloud service providers. This article presents a comprehensive analysis of a cloud migration framework that has been designed by adapting the novel security assessment principles of the offline risk assessment framework. The migration strategy has been modeled as a multiobjective optimization problem to further study the performance of numerous evolutionary algorithms in designing various cloud migration scenarios. The overall effectiveness of the proposed framework has been examined using a use-case application scenario and semisynthetic cloud service providers.     

Towards an autonomic performance management approach for a cloud broker environment using a decomposition–coordination based methodology

Efficient resource allocation of computational resources to services is one of the predominant challenges in a cloud computing environment. Furthermore, the advent of cloud brokerage and federated cloud computing systems increases the complexity of cloud resource management. Cloud brokers are considered third party organizations that work as intermediaries between the service providers and the cloud providers. Cloud brokers rent different types of cloud resources from a number of cloud providers and sublet these resources to the requesting service providers. In this paper, an autonomic performance management approach is introduced that provides dynamic resource allocation capabilities for deploying a set of services over a federated cloud computing infrastructure by considering the availability as well as the demand of the cloud computing resources. A distributed control based approach is used for providing autonomic computing features to the proposed framework via a feedback-based control loop. This distributed control based approach is developed using one of the decomposition–coordination methodologies, named interaction balance, for interactive bidding of cloud computing resources. The primary goals of the proposed approach are to maintain the service level agreements, maximize the profit, and minimize the operating cost for the service providers and the cloud broker. The application of interaction balance methodology and prioritization of profit maximization for the cloud broker and the service providers during resource allocation are novel contributions of the proposed approach.