一种面向连续数据保护的分布式存储模型研究

一个典型的连续数据保护(CDP,Continuous Data Protection)系统必须具有一个高效的存储系统来支撑.本文提出了一种分布式的存储模型,在此基础之上设计了一个面向CDP应用的分布式存储系统.同传统CDP系统相比,本文提出了分布式存储应用于CDP应用,将文件级和块级的数据存储都归纳到一个存储系统中;通过引入了元数据和数据分离的思想,并且把数据分级存储.根据原型系统的理论论证以及测试,结果表明,论文所提存储模型很好的解决了CDP服务器低效率,高负荷的问题,并且具有低成本的优势.     

Deleting files in the Celeste peer-to-peer storage system

Celeste is a robust peer-to-peer object store built on top of a distributed hash table (DHT). Celeste is a working system, developed by Sun Microsystems Laboratories. During the development of Celeste, we faced the challenge of complete object deletion, and moreover, of deleting “files” composed of several different objects. This important problem is not solved by merely deleting meta-data, as there are scenarios in which all file contents must be deleted, e.g., due to a court order. Complete file deletion in a realistic peer-to-peer storage system has not been previously dealt with due to the intricacy of the problem — the system may experience high churn rates, nodes may crash or have intermittent connectivity, and the overlay network may become partitioned at times. We present an algorithm that eventually deletes all file contents, data and meta-data, in the aforementioned complex scenarios. The algorithm is fully functional and has been successfully integrated into Celeste.     

蓝鲸分布式文件系统的客户端元数据缓存模型

在蓝鲸分布式文件系统中,客户端的所有元数据操作都是通过远程过程调用由元数据服务器完成,所有数据读写都是直接与存储服务器交换完成的.由于通信延迟,在客户端进行频繁数据读写时,元数据信息交换影响了整个系统的性能.我们设计了一种在客户端尽量缓存文件元数据信息的模型,有效地减少了元数据通信,缩短了整个读写过程的延迟,极大地提高了蓝鲸分布式文件系统的性能.     

Corslet: A shared storage system keeping your data private

With the exponential growth of digital data, it is becoming more and more popular to store data in shared distributed storage systems inside the same organization. In such shared distributed storage systems, an ordinary user usually does not have the control permission over the whole system, and thus cannot secure data storage or data sharing of his own files. To solve this issue, this paper proposes a new system architecture to secure file storing and sharing efficiently over untrusted shared storage and n...     

面向点对点的安全可靠存储系统

利用P2P的方法实现了一个共享和合作的安全存储系统,其中参与节点运行Paramecium协议或其他兼容的DHT(distributed hash table)协议形成自组织覆盖层,维护系统的组织结构和提供路由服务.由于该系统为开放式结构,引入了基于PKI的安全认证机制以确保用户数据的授权访问.用户数据和副本标示的绑定支持了安全的数据自修复;副本类型的引入提供了安全的共享写.初步的分析和实验表明,该P2P系统在现实条件下,在消耗较低的维护带宽的同时维持了较高的可靠性并提供了较好的读写性能.     

基于磁光虚拟存储系统的文件调度算法

基于光盘库的Hadoop分布式文件系统（HDFS光盘库）在单位存储成本、数据安全性、使用寿命等方面非常符合当前大数据存储要求，但是HDFS不适合存储大量小文件和实时数据读取。为了使HDFS光盘库能更好地运用到更多大数据存储场景，本文提出一种更加适合大数据存储的磁光虚拟存储系统（MOVS, Magneto-optical Virtual Storage System）。系统在HDFS光盘库与用户之间加入磁盘缓存，并在磁盘缓存内通过文件标签分类、虚拟存储、小文件合并等技术将磁盘缓存内小文件合并为适合HDFS光盘库存储的大文件，提高系统的数据传输速度。系统还使用了文件预取、缓存替换等文件调度算法对磁盘缓存内文件进行动态更新，减少用户访问HDFS光盘库次数。实验结果表明，MOVS相对HDFS光盘库在响应时间和数据传输速度方面得到很大改善。     

IKAROS: An HTTP-Based Distributed File System, for Low Consumption & Low Specification Devices

We present the design of IKAROS: an HTTP-based distributed file system, which provides file access scalability and targets a large variety of operating systems and storage systems. IKAROS bypasses the server bottleneck enabling clients to access storage directly, while supporting the usage of multiple types of meta-data. It enables low-consumption, low-specification and low-cost devices to achieve a high throughput data transfer, responding to highly demanding applications. We present data transfer results comparing IKAROS, NFS, PVFS2 and HDFS on a Small Office/Home Office Network Attached Storage infrastructure. We show that IKAROS architecture satisfies and outperforms the data rate demands of high performance applications. We also present experimental results which compare IKAROS and GridFTP using the European Grid Infrastructure. IKAROS performs better in most cases while being competitive at the rest.     

ROARS: a robust object archival system for data intensive scientific computing

As scientific research becomes more data intensive, there is an increasing need for scalable, reliable, and high performance storage systems. Such data repositories must provide both data archival services and rich metadata, and cleanly integrate with large scale computing resources. ROARS is a hybrid approach to distributed storage that provides both large, robust, scalable storage and efficient rich metadata queries for scientific applications. In this paper, we present the design and implementation of ROARS, focusing primarily on the challenge of maintaining data integrity across long time scales. We evaluate the performance of ROARS on a storage cluster, comparing to the Hadoop distributed file system and a centralized file server. We observe that ROARS has read and write performance that scales with the number of storage nodes, and integrity checking that scales with the size of the largest node. We demonstrate the ability of ROARS to function correctly through multiple system failures and reconfigurations. ROARS has been in production use for over three years as the primary data repository for a biometrics research lab at the University of Notre Dame.     

A scalable bandwidth guaranteed distributed continuous media file system using network attached autonomous disks

Repository for continuous media data differs from that of the traditional text-based data both in storage space and streaming bandwidth requirements. The file systems used for continuous media streams need to support large volumes and high bandwidth. We propose a scalable distributed continuous media file system built using autonomous disks. Autonomous disks are attached directly to the network and are able to perform lightweight processing. We discuss different ways to realize the autonomous disk, and describe a prototype implementation on a Linux platform using PC-based hardware. We present the basic requirements of the continuous media file system and present the design methodology and a prototype Linux-based implementation of the distributed file system that supports the requirements. We present experimental results on the performance of the proposed file system prototyped using autonomous disks. We show that the performance of the file system scales linearly with the number of disks and the number of clients. The file system performs much superior to NFS running on the same hardware platform and can deliver higher raw disk bandwidth to the applications. We also present bandwidth and time sensitive read/write procedures for the file system and show that the file system can provide strict bandwidth guarantees for continuous media streams.     

一种数据本地化存储与处理系统

简述了当前大型数据中心普遍采用的计算节点集群与存储系统模块化设计的系统结构,说明了部署在各模块上的主要集群系统。分析了具有独立性的结构化数据本地化存储于计算节点的可能性,给出了系统基本框架,从总体拥有成本（TCO）的角度分析了其价值。结合高能物理研究的原始数据特点,认为数据本地化存储在节点上,有利于提高整体利用率,指出了关键部件——文件元数据管理系统的设计要点,分析了PBS作业批处理系统集成文件元数据管理系统的三种方案,给出第一种方案的详细设计,相应的用户提交作业方式的改变。在测试环境下,初步部署了文件元数据管理系统,测试了三种集成方案,给出了简要的分析比较。     

SFFS：低延迟的面向小文件的分布式文件系统

社交网站和电子商务等网络服务发展迅速,这类服务需要存储大量图片、音乐、微博文本等小文件。传统的分布式存储系统,如HDFS（Hadoop distributed file system）,是面向大文件而设计的,在存储小文件时会产生元数据开销过大,访问延迟较高等问题,不能适应存储海量小文件的应用环境。分析了TFS（Taobao file system）的系统架构和读写流程,发现TFS在每次读/写过程中至少要建立3次网络连接,增大了读写延迟。针对海量小文件存储带来的挑战和TFS存在的问题,提出了一种新的低延迟、高可用的面向海量小文件的分布式存储方案,并实现了分布式文件系统SFFS（small-file file system）。性能测试表明,SFFS和TFS相比,写延迟降低了76.6%,读延迟降低了约10%。通过对系统结构的分析,相比于TFS,SFFS在中心节点的负载更轻,失效恢复更快,在可用性方面更有优势。     

高性能逻辑文件系统设计与实现

服务器端文件系统不仅需要很大的容量,而且要为大量并发访问提供很高的I/O性能。该文提出一种把多个物理文件系统通过软件集成为一个逻辑文件系统的技术,很好地聚合了各个文件系统所在磁盘设备的带宽和容量,综合了不同文件系统在元数据和数据处理性能上的优势。性能测试结果表明,逻辑文件系统技术是一种构造支持高度并发访问的高性能文件系统的有效方法。     

一个网络文件存储系统TNS关键技术

基于多服务器架构、为多用户服务的网络文件存储系统普遍存在资源分配不均,重复文件多,存储空间浪费严重的问题。设计并实现了TNS网络文件存储系统,该系统基于多服务器存储架构,分别由用户服务器、索引服务器、数据服务器、共享服务器、管理服务器和登录服务器组成,为多用户服务,采用一致性Hash实现负载均衡,支持在客户端进行文件粒度的重复数据删除。经过实际生产环境运行测试,具有良好的负载均衡能力和重复数据删除功能,可以有效节省存储空间,提高存储设备利用率。     

Data Utilization-Based Adaptive Data Management Method for Distributed Storage System in WAN Environment

Recently, research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase. Physical expansion limits exist for traditional standalone storage systems, such as I/O and file system capacity. However, the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data location. And this leads to system performance degradation due to low locality occurring in a Wide Area Network (WAN) environment with high network latency. This problem hinders deploying distributed storage systems to multiple data centers over WAN. It lowers the scalability of distributed storage systems to accommodate data storage needs. This paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system performance. The proposed distributed storage method monitors data utilization and locality to classify data temperature as hot, warm, and cold. With assigned data temperature, the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment, leading to overall system performance degradation. This paper also conducts simulations to evaluate the proposed and existing distributed storage methods. The result shows that our proposed method reduced latency by 38% compared to the existing method. Therefore, the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods, such as consistent hashing.     

SonD系统中虚拟存储设备共享Cache方法的设计

蓝鲸服务点播系统(SonD系统)是一个基于网络存储的新型计算环境,为大规模计算环境中计算机的部署和管理提供了有效手段。SonD系统中虚拟存储设备共享cache方法,在文件一级实现了对SonD系统中虚拟存储设备间物理共享数据在内存中cache的共享,有效地避免了共享cache后带来的数据的一致性问题。实验数据表明,虚拟存储设备共享cache方法提高了SonD系统的整体性能。     

面向异构DHT存储的数据与位置解耦算法

分布式哈希表(distributed hash table, DHT)由于其高效的数据寻址方式而被广泛应用于分布式存储.传统DHT必须将数据存放在指定节点中才能实现高效的数据分布式寻址,极大地限制了DHT技术的应用范围.例如,在异构存储网络中,节点的存储空间、带宽、稳定性等均有较大差异,结合数据特征和节点性能差异选择合适的数据存放节点可以很大程度上提高数据的访问效率,而传统DHT数据和存储位置紧耦合的特征导致其难以应用于异构的存储网络中.针对此问题,提出了vRoute算法以实现DHT中数据标识与其存储位置的解耦.通过构建基于Bloom Filter的分布式数据索引, vRoute算法可以在不降低数据寻址效率的基础上允许数据存储在网络中的任意节点.通过扩展Kademlia算法实现了vRoute,并从理论上证明了vRoute算法的有有效性.最后,模拟实验表明vRoute以较低的存储、网络开销实现了和传统的DHT算法接近的数据寻址效率.     

基于DHT的拓扑感知节点聚集算法

在文件共享、流媒体和协作计算等P2P应用模型中,节点间采用单播通信并构建出对应的覆盖网络.由于覆盖网络通常建立在已有的底层网络之上,节点随机加入系统将导致上下层网络拓扑不匹配,不仅增加了节点间通信延时而且给底层网络带来较大的带宽压力.当前的拓扑匹配算法尚存在可扩展性低、节点聚集时延长等问题.在网络坐标算法和DHT算法基础之上,提出一种分布式的拓扑感知节点聚集算法TANRA,利用等距同心圆簇对节点二维网络坐标平面进行等面积划分,并根据节点所处区域进行多层命名空间中区间的一一映射.由于保留了节点之间的邻近关系,从而可使用DHT基本的"发布"和"搜索"原语进行相邻节点聚集.仿真结果表明,TANRA算法在大规模节点数时能有效保证网络拓扑匹配,并且具有较低的加入延时.     

Hybrid hierarchy storage system in MilkyWay-2 supercomputer

With the rapid improvement of computation capability in high performance supercomputer system, the imbalance of performance between computation subsystem and storage subsystem has become more and more serious, especially when various big data are produced ranging from tens of gigabytes up to terabytes. To reduce this gap, large-scale storage systems need to be designed and implemented with high performance and scalability.MilkyWay-2 (TH-2) supercomputer system with peak performance 54.9 Pflops, definitely has this kind of requirement for storage system. This paper mainly introduces the storage system in MilkyWay-2 supercomputer, including the hardware architecture and the parallel file system. The storage system in MilkyWay-2 supercomputer exploits a novel hybrid hierarchy storage architecture to enable high scalability of I/O clients, I/O bandwidth and storage capacity. To fit this architecture, a user level virtualized file system, named H²FS, is designed and implemented which can cooperate local storage and shared storage together into a dynamic single namespace to optimize I/O performance in IO-intensive applications. The evaluation results show that the storage system in MilkyWay-2 supercomputer can satisfy the critical requirements in large scale supercomputer, such as performance and scalability.     

智能手机中基于云实例的高效文件管理系统

针对现有云存储技术应用到智能手机上导致其能量和带宽消耗较大这一不足,以Dropbox平台作为云服务提供商,设计了一种基于云实例的高效安全的文件管理系统（FM-CI）。FM-CI支持下载、压缩、加密和转换操作,同时支持在两个智能手机用户的云存储空间之间进行文件传输。此外,考虑到频繁开启云实例仍然可能增加用户的成本,还给出了用户空闲实例共享协议和基于共享实例的文件传输方案。仿真实验结果表明,FM-CI能在消耗较少时间和带宽的前提下,高效地完成文件操作,且性能要优于目前最新的云存储方案。     

基于区块链的可溯源政务大数据共享方法研究

政务服务跨域协作是政府数字化转型和跨域治理相结合所催生的新型治理模式,是政务服务治理的价值目标。由于政府各部门的具体业务和功能不同,各部门都有一套独立的数据管理系统,且各信息化系统存储多样、数据格式复杂、业务流程各异。如何安全可靠地实现各个部门之间的数据共享已成为一项研究难点。传统政务数据共享通常采用集中式共享模式,该模式容易引发数据隐私泄露、部门权限混乱、单点故障等一系列问题。为解决上述问题,该文提出了一种属性基加密与区块链结合的政务数据共享方案。首先,由数据拥有者制定访问控制策略,对数据请求者的属性进行限制;然后,利用子集覆盖技术,实现数据安全共享中的细粒度访问控制及密钥更新,结合线性秘密共享,以实现访问策略的完全隐藏,采用星际文件系统分布式网络存储对称加密后的密文,以缓解区块链系统的存储压力;最后,利用 Keccak 算法对检索数据密文的哈希值进行重加密,实现数据的完整性验证。通过安全性分析和相关实验可知,该文所提方案在安全性和效率方面均能满足政务数据安全共享的需求,可实现政务数据的高效、安全和可溯源共享。