Data access control policy of encrypted deduplication system

To solve the problem that convergent encryption was commonly used in existing encrypted deduplication systems in cloud storage and data owner couldn’t effectively enforce access control on their outsourced data,an encrypted deduplication system was proposed to support access control functions such as identity authentication,authorization deduplication and the update of access control policy.The outsourced data was only deduplicated with the authorized users,and the unauthorized users couldn’t obtain any data information.CP-ABE and the partition of the ElGamal private key were used to update the access control policy of data.Self-control objects was used to encapsulate user’s data and its access policy,providing authentication for data visitors and ensuring the access control policies enforced effectively.Security analysis and simulation results demonstrate that the proposed system enables data access control and executes efficiently.     

Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

Deduplication 通常在两个企业存储系统和云存储被使用了。克服性能挑战为选择恢复 deduplication 系统的操作, solid-state-drive-based (即,基于 SSD ) 读的缓存能为由缓冲加快被部署流行动态地恢复内容。不幸地,经常的数据更改由古典缓存计划导致了(例如, LRU 和 LFU ) 显著地弄短 SSD 一生当在 SSD 减慢 I/O 进程时。处理这个问题,我们建议新解决方案砍缓存极大地由扩大比例象 I/O 性能一样改进 SSD 的 write 耐久性长期流行(砍) 在写进基于 SSD 的缓存的数据之中的数据。砍缓存保留很长时间在 SSD 缓存砍数据减少的时期缓存代替的数字。而且,它在 deduplication 集装箱阻止不得人心或不必要的数据被写进 SSD 缓存。我们在一个原型 deduplication 系统实现了砍缓存评估它的性能。我们的试验性的结果显示砍缓存弄短潜伏选择与仅仅 deduplicated 数据的 5.56% 能力以小基于 SSD 的缓存的成本由 37.3% 的一般水准恢复。重要地,砍缓存由 9.77 的一个因素改进 SSD 一生。砍缓存为一个成本效率的基于 SSD 的读的缓存解决方案提供到的证据表演增加性能选择为 deduplication 恢复系统。     

Efficient image restoration of virtual machines with reference count based rewriting and caching

Virtual machine (VM) image backups have duplicate data blocks distributed in different physical addresses, which occupy a large amount of storage space in a cloud computing platform (Choo et al.,  [1] and González-Manzano et al.,  [2]). Deduplication is a widely used technology to reduce the redundant data in a VM backup process. However, deduplication always causes the fragmentation of data blocks, which seriously affects the VM restoration performance. Current approaches often rewrite data blocks to accelerate image restoration, but rewriting could cause significant performance overhead because of frequent I/O operations. To address this issue, we have found that the reference count is a key to the fragmentation degree from a series of experiments. Thus, we propose a reference count based rewriting method to defragment VM image backups, and a caching method based on the distribution of rewritten data blocks to restore VM images. Compared with existing studies, our approach has no interfere to the deduplication process, needs no extra storage, and efficiently improves the performance of VM image restoration. We have implemented a prototype to evaluate our approach in our real cloud computing platform OnceCloud. Experimental results show that our approach can reduce about 57% of the dispersion degree of data blocks, and accelerate about 51% of the image restoration of virtual machines.     

一种基于聚类分组的虚拟机镜像去冗余方法

随着云计算的兴起,虚拟化技术使用也越来越广泛,虚拟机正逐步取代物理机,成为应用服务的部署环境.出于灵活性、可靠性等方面的需求,虚拟机镜像急剧增长,如何高效地、经济地管理这些镜像文件已成为一个很有挑战性的研究热点.由于虚拟机镜像之间存在大量重复性的数据块,高效的去冗余方法对于虚拟机镜像管理至关重要.然而,传统的去冗余方法由于需要巨大的资源开销,会对平台中托管的虚拟机性能造成干扰,因而并不适用于云环境.提出了一种局部去冗余的方法,旨在优化镜像去冗余过程.其核心思想是:将全局去冗余变成局部去冗余,从而降低去冗余算法的空间复杂度,以达到减少操作时间的目的.该方法利用虚拟机镜像相似性作为启发式规则对虚拟机镜像进行分组,当一个新的镜像到来时,通过统计抽样的方法为镜像选取最为相似的分组进行去冗余.实验结果表明:该方法可以通过牺牲1%左右的存储空间,缩短50%以上的去冗余操作时间.     

用户感知的重复数据删除算法

通过大量的实验分析发现:在云桌面场景下,数据拥有者之间的工作相关度越大,则该用户之间存在重复数据的概率越大.基于该实验结果,提出了用户感知的重复数据删除算法.该算法打破了数据空间局部性特征的限制,实现了以用户为单位的更粗粒度的查重计算,可以在不影响重删率的前提下,减少5~10倍常驻内存指纹的数量,并可将每次查重计算的指纹检索范围控制在一个常数范围内,不随数据总量的增加而线性增加,从而有效避免了因为数据总量增加而导致内存不足的问题.除此之外,该算法还能根据存储系统的负载情况自动调整重复指纹检索范围,在性能与重删率之间加以平衡,从而更好地满足主存储场景的需要.原型验证表明,该算法可以很好地解决云计算场景下海量数据的重复数据删除性能问题.与OpenDedup算法相比,当数据指纹总量超出内存可用空间时,该算法可以表现出巨大的优势,减少200%以上的读磁盘操作,响应速度提升3倍以上.     

基于动态bloom filter的云存储安全去重方案

现有的所有权证明去重方案容易遭受诚实但好奇服务器的威胁影响,借助可信第三方解决该问题将导致开销过大。基于动态bloom filter提出一种改进的、无须可信第三方的所有权证明安全去重方案,采用收敛加密算法抵抗诚实但好奇的服务器,并通过服务器检查数据块密文和标签的一致性来防止数据污染攻击。此外,采用密钥链机制对收敛密钥进行管理,解决了现有方案中收敛密钥占用过多存储空间的问题。分析与比较表明,该方案具有较小的密钥存储开销和传输开销。     

相似索引：适用于重复数据删除的二级索引

由于EB（extreme binning）使用文件的最小块签名作为文件的特征, 它不适合处理主要包括小文件的数据负载, 会导致较差的重复数据删除率。为了改进EB, 提出了相似索引。它把相似哈希作为文件的特征, 是一种适用于以小文件为主的数据负载的重复数据删除的二级索引。实验结果表明, 相似索引的重复数据删除率比EB高24. 8%; 相似索引的内存使用量仅仅是EB的0. 265%。与EB相比, 相似索引需要更少的存储使用量和内存使用量。     

SBBS: A sliding blocking algorithm with backtracking sub-blocks for duplicate data detection

With the explosive growth of data, storage systems are facing huge storage pressure due to a mass of redundant data caused by the duplicate copies or regions of files. Data deduplication is a storage-optimization technique that reduces the data footprint by eliminating multiple copies of redundant data and storing only unique data. The basis of data deduplication is duplicate data detection techniques, which divide files into a number of parts, compare corresponding parts between files via hash techniques and find out redundant data. This paper proposes an efficient sliding blocking algorithm with backtracking sub-blocks called SBBS for duplicate data detection. SBBS improves the duplicate data detection precision of the traditional sliding blocking (SB) algorithm via backtracking the left/right 1/4 and 1/2 sub-blocks in matching-failed segments. Experimental results show that SBBS averagely improves the duplicate detection precision by 6.5% compared with the traditional SB algorithm and by 16.5% compared with content-defined chunking (CDC) algorithm, and it does not increase much extra storage overhead when SBBS divides the files into equal chunks of size 8 kB.     

基于Swift的可去重校园云存储系统的设计与实现

随着高校信息化的发展以及教学、科研和管理应用系统的广泛应用,数据资源如:图片、文档、视频等非结构化资源增长十分迅速。如何应对校园网络环境中不断增大的存储需求,提高存储资源的利用效率,是校园数据中心运维中一个比较重要的问题。本文介绍了基于开源软件 Swift 的云存储平台的搭建,以及带有重复数据删除功能的校园云存储系统(Dedupe_swift) 的设计与实现。通过重复数据删除功能的引入,提高了底层存储空间利用率;采用源端去重机制,为用户缩短了重复文件的上传时间;通过 Web 服务将存储作为服务提供给用户,为用户提供良好的云存储访问体验。