BW-VSDS:大容量、可扩展、高性能和高可靠性的网络虚拟存储系统

设计并实现了一个大容量、可扩展、高性能和高可靠性的网络虚拟存储系--BW-VSDS.和其他网络存储系统对比,它有如下的特点:1)采用带内元数据管理和带外数据访问的虚拟存储管理架构,存储管理更灵活,并且系统扩展性更好;2)在单个节点内部的多个虚拟卷、多个虚拟池和多个网络存储设备上利用存储虚拟化技术重构得到面向多种存储应用的网络虚拟存储设备,实现了3层的层次化存储虚拟化模型,对内共享存储设备的容量和带宽,对外提供不同属性的虚拟磁盘;3)采用写时按需分配策略提高了存储空间的利用率,使用数据块重组提高了I/O读写性能;4)使用设备链表和位图实现了层叠式虚拟快照,支持增量快照、写时拷贝和写时重定向机制,实现源卷和快照卷的数据共享;5)提出结合带外存储虚拟化管理的后端集中的带外冗余管理结构,数据读写直接访问存储节点,冗余管理节点在磁盘上以日志方式缓存从存储节点镜像写的数据,然后在后台进行RAID5冗余计算,提高了活跃数据的可靠性,减轻了冗余计算对写性能的影响.     

去中心化的分布式存储模型

分布式数据存储过程中的元数据保存在中心节点上,容易造成单点故障和易被恶意修改,安全性较差。虽然,通过引入备份节点可以一定程度上避免该问题,但节点之间的同步和切换效率较低。同时,存储元数据的节点可以达成共识修改元数据,缺乏可信性。针对传统分布式存储中存在的问题,结合区块链的特点,提出一种去中心化的分布式存储模型DMB（Decentralized Metadata Blockchain）,通过将元数据保存在区块中、冗余存储区块链、协作验证来保证元数据的完整性。模型分为两个阶段,即元数据存储阶段和元数据验证阶段。在元数据存储阶段,将用户的签名和副本位置数据发送给若干验证节点,生成元数据区块并写入元数据区块链中。在元数据验证阶段,验证节点首先检查本地元数据区块链的状态和全局状态是否相同,如果不相同则进行状态同步。然后,检索本地元数据区块链来验证元数据完整性。理论与实验结果表明,DMB模型可以保证元数据的可追溯性和完整性,有较好的并发处理能力,对数据存储的效率影响较小。     

Practices of backuping homomorphically encrypted databases

Ideal homomorphic encryption is theoretically achievable but impractical in reality due to tremendous computing overhead. Homomorphically encrypted databases, such as CryptDB, leverage replication with partially homomorphic encryption schemes to support different SQL queries over encrypted data directly. These databases reach a balance between security and efficiency, but incur considerable storage overhead, especially when making backups. Unfortunately, general data compression techniques relying on data similarity exhibit inefficiency on encrypted data. We present CryptZip, a backup and recovery system that could highly reduce the backup storage cost of encrypted databases. The key idea is to leverage the metadata information of encryption schemes and selectively backup one or several columns among semantically redundant columns. The experimental results show that CryptZip could reduce up to 90.5% backup storage cost on TPC-C benchmark.     

Version-vector based video data online cloud backup in smart campus

As a kind of popular multimedia service, the video business in smart campus is fully developed, and the data volume is experiencing a continuous tremendous growth. However, the video data storage also needs to cope with many security threats resulted from some uncertainly factors. Recently, data backup has been credited as an important method to address these issues. Yet, how to improve the efficiency of video data online backup remains a critical challenge. In the context of cloud computing, a version-vector based video data online backup system is proposed in this paper, which fully implements three important functions, e.g., backup, indexing and recovery. At the time of the client backuping the data to the server, the version vectors of files and data chunks are produced as metadata. Accordingly, only through exchanging a small amount of metadata between the data nodes, it can identify the redundant data such that breaking the limitations on file-level deduplication and data-chunk-level deduplication in the existing backup systems, and can further improve the performance of backup, indexing and recovery. The conducted experiments have been shown to demonstrate the efficiency.

     

The distributed data center: front-end solutions

The need to provide reliable data availability has always driven data center design. Redundant storage systems and backup mechanisms are essential for enterprises to provide 24/7 data access. Using a redundant array of independent disks (RAID) and data server clusters helps prevent catastrophic data loss. However, redundancy within the data center itself represents only a partial solution. Although backup mechanisms preserve data, you have a high-availability solution only if redundancy also protects against a network failure between the center and the outside world. A distributed data center provides such a solution.     

云环境下元数据弹性分级一致性保障机制研究 *

云存储环境下,元数据提供数据定位与资源描述服务,云存储系统利用副本技术保障元数据的可用性。为了解决元数据的动态更新导致的元数据副本的不一致,本文从云存储系统中不同元数据对一致性强度需求的差异性出发,提出了基于元数据弹性分级的一致性保障机制。根据元数据访问频率以及更新属性敏感度对元数据更新弹性分级,结合基于读优化的Quorum算法实现元数据更新的分级一致性保障。实验证明该机制可以有效权衡元数据副本可用性、一致性和系统开销,相比单一的一致性策略更为灵活。     

基于模糊预测的数据复制优化模型的研究

云数据处理系统中广泛采用了多数据副本复制技术,以防止数据丢失,如果数据复制的份数或位置不当,就会引起数据的可用性小于用户期望的数据可用性或存储空间的浪费（如复制份数过多）。针对该问题,经研究提出了一种基于模糊预测的数据复制优化模型,该模型由模糊预测模块和复制优化模块组成。模糊预测模块以节点信息（CPU信息、节点带宽信息、内存信息和硬盘信息）作为输入,预测出节点的可用性;复制优化模块把节点的可用性和用户期望的数据可用性作为输入,计算出在满足用户期望情况下数据复制的份数和位置。提出的复制优化模型能根据云数据存储系统中数据节点可用性实现动态的优化数据复制,能获得较高的存储性价比。模拟实验中基于模糊预测的数据复制优化模型策略需要的存储空间分别是Hadoop策略的42．62％,42．84％,但文件的平均可用性可达到88．69％,90．54％,表明提出的基于模糊预测的复制模型实现了在节省存储空间的同时保证了文件可用性。     

主动容错副本存储系统的可靠性分析模型

主动容错机制通过预先发现即将故障的硬盘来提醒系统提前迁移备份危险数据,从而显著提高存储系统的可靠性。针对现有研究无法准确评价主动容错副本存储系统可靠性的问题,提出几种副本存储系统的状态转换模型,然后利用蒙特卡洛仿真算法实现了该模型,从而模拟主动容错副本存储系统的运行,最后统计系统在某个运行时期内发生数据丢失事件的期望次数。采用韦布分布函数模拟设备故障和故障修复事件的时间分布,并定量评价了主动容错机制、节点故障、节点故障修复、硬盘故障以及硬盘故障修复事件对存储系统可靠性的影响。实验结果表明,当预测模型的准确率达到50%时,系统的可靠性可以提高1~3倍;与二副本系统相比,三副本系统对系统参数更敏感。所提模型可以帮助系统管理者比较权衡不同的容错方式以及系统参数下的系统可靠性水平,从而搭建高可靠和高可用的存储系统。     

基于服务部署的高可用模型及其可用性分配算法

传统高可用系统存在可扩展性较差的问题.文中提出基于服务部署的高可用系统模型,利用对计算资源与存储资源的分离管理实现虚拟高可用服务节点,通过不同服务间共享冗余资源提高资源利用率,有效解决可扩展性问题.基于服务部署高可用系统的关键问题为如何根据服务可用性期望值和使用模式等合理分配资源.针对本系统可用性分配特点,文中提出了最适合冗余优先分配算法,基于结合费用与惩罚值的目标函数得到满足需求的相对最优解,实验证明此算法能较好地达到系统的实时服务部署要求.     

A data-check based distributed storage model for storing hot temporary data

For the purpose of ensuring data security, traditional systems have widely used redundancy backup to store multiple copies of data. Multiple copies technology has high reliability, but also has the disadvantage of high redundancy storage and low space utilization. On the contrary, EC (Erasure Coding) technology has a high utilization rate of storage space, but the overhead of coding, decoding and data reconstruction is great. So, this paper demonstrates a data backup method based on XOR checksum being suitable for storing hot temporary data, which first splits the data into two parts and then performs the XOR operation of the two parts to generate another part of the data. Finally, the XOR checksum stores the three data parts into different nodes. The checksum not only ensures the security of data but also saves the storage space, thus improving the performance of reading and writing. This strategy achieves a mutual backup between the three nodes in order to ensure data security. Because there is only one copy of original data in the system, this model resolves the data inconsistency problem reasonably and simplifies the data version control existing in the redundancy backup model. Actual data test results show that, compared with the current mainstream Cassandra redundant backup model, the performance of a data backup model based on the XOR checksum proposed and implemented in this paper has been significantly improved: the reading performance improves by an average of 10%, and the writing performance improves by an average of 30%.     

面向海量非结构化数据的非关系型存储管理机制

针对传统的关系数据存储系统性能不足、容错性差,无法适应海量非结构化数据管理的问题,提出一种高性能、高可用非关系型存储管理机制。首先,设计了良好的用户访问服务接口,通过高效的一致性哈希算法支持数据分发到多个存储节点;其次,采用可配置的数据副本机制改善存储系统的可用性;最后,提出查询故障处理机制,用以提升存储系统的容错性,避免节点失效导致服务中断问题。实验结果表明,在不同规模用户负载下,新的存储系统的并发访问请求能力和传统的文件系统、关系数据库相比,分别提升了30%和50%;同时,在合理响应时间内,故障状态下的存储系统的可用性损失小于14%。因此,该机制适用于海量非结构化数据的高效存储管理。     

服务链联合多元化及动态冗余备份方法研究

针对电力云环境下虚拟网络函数(virtual network funtion,VNF)单一集偶发系统故障致使服务链(service function chain,SFC)进程开环的难题,在MATLAB仿真实验平台开展了分布式多集和定制冗余解决方法的研究.通过分析异构VNF服务可靠性需求,明确单一集结构微服务部署架构,利用异地部署实现VNF轻型分布,并采用定制冗余策略,进一步提升VNF弹性.结合轻型多集和冗余定制方法的优势,提出了混合整数线性模型的服务链配置新策略.以资源消耗率、服务中断率、服务链可用性为对象参数的实验结果表明,改进算法可有效避免系统故障导致的服务链完全中断,在满足目标服务链可用性水平和提升服务链弹性能力的同时,降低了平台系统多元化及冗余备份成本.     

Contextual Trust Aided Enhancement of Data Availability in Peer-to-Peer Backup Storage Systems

Peer-to-peer storage services are a cost-effective alternative for data backup. A basic question that arises in the design of such systems is: In which peers do we store redundant data? Choosing appropmailriate peers for data backup is important at a microscopic level, from an end-user’s perspective to guarantee good performance, e.g., quick access, high availability, etc., as well as at a macroscopic level, e.g., for system optimization, fairness, etc. Existing systems apply different techniques, including random selection, based on a distributed hash table (DHT) or based on the peers’ past availability pattern. In this paper, we propose as an alternative, a contextual trust based data placement scheme to select suitable data holders. It is originally designed for and applicable to scenarios where there is inadequate historical information about peers, a common scenario in large-scale systems. Specifically, our scheme estimates trustworthiness of a peer based on stereotypes, formed by aggregating information of interactions with other (similar) peers. Simulation experiments show that our placement scheme outperforms not only random selection but also schemes using historical information, in terms of both achieved data availability as well as bandwidth overheads to sustain the system.     

RAID0.5: design and implementation of a low cost disk array data protection method

RAID has long been established as an effective way to provide highly reliable as well as high-performance disk subsystems. However, reliability in RAID systems comes at the cost of extra disks. In this paper, we describe a mechanism that we have termed RAID0.5 that enables striped disks with very high data reliability but low disk cost. We take advantage of the fact that most disk systems use offline backup systems for disaster recovery. With the use of these offline backup systems, the disk system needs to only replicate data since the last backup, thus drastically reducing the storage space requirement. Though RAID0.5 has the same data loss characteristics of traditional mirroring, the lower storage space comes at the cost of lower availability. Thus, RAID0.5 is a tradeoff between lower disk cost and lower availability while still preserving very high data reliability. We present analytical reliability models and experimental results that demonstrate the enhanced reliability and performance of the proposed RAID0.5 system.     

Adaptive and scalable load balancing for metadata server cluster in cloud-scale file systems

Big data is an emerging term in the storage industry, and it is data analytics on big storage, i.e., Cloud-scale storage. In Cloud-scale (or EB-scale) file systems, load balancing in request workloads across a metadata server cluster is critical for avoiding performance bottlenecks and improving quality of services.Many good approaches have been proposed for load balancing in distributed file systems. Some of them pay attention to global namespace balancing, making metadata distribution across metadata servers as uniform as possible. However, they do not work well in skew request distributions, which impair load balancing but simultaneously increase the effectiveness of caching and replication. In this paper, we propose Cloud Cache (C²), an adaptive and scalable load balancing scheme for metadata server cluster in EB-scale file systems. It combines adaptive cache diffusion and replication scheme to cope with the request load balancing problem, and it can be integrated into existing distributed metadata management approaches to efficiently improve their load balancing performance. C² runs as follows: 1) to run adaptive cache diffusion first, if a node is overloaded, loadshedding will be used; otherwise, load-stealing will be used; and 2) to run adaptive replication scheme second, if there is a very popular metadata item (or at least two items) causing a node be overloaded, adaptive replication scheme will be used, in which the very popular item is not split into several nodes using adaptive cache diffusion because of its knapsack property. By conducting performance evaluation in trace-driven simulations, experimental results demonstrate the efficiency and scalability of C².     

Fault tolerant algorithm based on dynamic and active load balancing for redundant services

A new Some-Read-Any-Write (SRAW) fault tolerant algorithm for redundant services is presented that allows a system to adjust failures dynamically in order to keep the availability and improve the performance.SRAW is based upon dynamic and active load balancing. By introducing dynamic and active load balancings cheme into redundant services, not only the processing speed of requests can be greatly improved, but also the load balancing can be simply and efficiently achieved. Integrated with consistency protocol in this paper, SRAW can also be applied to state services. The performance of SRAW algorithm is also analyzed, and comparisons with other fault tolerant algorithms, especially with RAWA, indicate that SRAW efficiently improves the performance of redundant services with guaranteeing system availability.     

并行文件系统集中式元数据管理高可用系统设计

本文针对并行文件系统集中式元数据管理存在单一失效点的问题，设计了一种双元服务器高可用系统。通过元数据镜像生成冗余副本，保证了元数据的可靠性；通过故障屏蔽，实现了对客户端的连续服务。所有功能都是用内核模块来实现，因此对用户是完全透明的。最后，本文阐述了各个模块的实现，并对该系统进行了可靠性分析。     

高顽存性存储网格模型

结合网格技术和存储技术,提出了基于域的存储网格模型。在域模型架构下,深入分析了域划分原则、元数据目录服务和数据容错等问题,重点探讨了数据高顽存性存储策略的设计。在比较复制、分片冗余等不同数据冗余策略特点的基础上,提出了一种混合式的数据冗余策略。模拟实验表明它兼具复制策略和分片冗余策略的优点,既保持了存储数据的整体可靠性水平,又弥补了低冗余度下编码复制可靠性偏低的不足,使冗余存储数据具有更好的可靠性效果。     

高可用性软件的设计与实现

从高可用性系统的类型和功能出发，在分析系统失效因素和高可用系统结构的基础上，设计了一个高可用性的软件结构系统，它可以运行于主备、双机和多机3种工作模式，并以有限状态机的状态转移图，具体实现了链路管理和业务接管等关键技术，最后将所设计的软件应用到实际的系统之中，能够很好地满足需求。     

Randomized Protocols for Duplicate Elimination in Peer-to-Peer Storage Systems

Distributed peer-to-peer systems rely on voluntary participation of peers to effectively manage a storage pool. In such systems, data is generally replicated for performance and availability. If the storage associated with replication is not monitored and provisioned, the underlying benefits may not be realized. Resource constraints, performance scalability, and availability present diverse considerations. Availability and performance scalability, in terms of response time, are improved by aggressive replication, whereas resource constraints limit total storage in the network. Identification and elimination of redundant data pose fundamental problems for such systems. In this paper, we present a novel and efficient solution that addresses availability and scalability with respect to management of redundant data. Specifically, we address the problem of duplicate elimination in the context of systems connected over an unstructured peer-to-peer network in which there is no a priori binding between an object and its location. We propose two randomized protocols to solve this problem in a scalable and decentralized fashion that does not compromise the availability requirements of the application. Performance results using both large-scale simulations and a prototype built on PlanetLab demonstrate that our protocols provide high probabilistic guarantees while incurring minimal administrative overheads.