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Coding and Maintenance Strategies for Cloud Storage: Correlated Failures, Mobility and Architecture Awareness

机译:云存储的编码和维护策略:相关的故障,移动性和体系结构意识

摘要

As a result of evergrowing data and recent interest in storing and analyzing it, distributed storage systems (DSS), which is also known as cloud storage, have become one of the most important research areas in the literature. Not only such networks are being used as backbone systems for companies like Google, Microsoft and Facebook but also they have accelerated the growth of cloud computing, which is an essential business line for institutions such as IBM, Amazon and Salesforce. In this dissertation, the focus is on the storage side of cloud in order to address the important questions in designing such systems. First, coding theoretic approach is taken to handle correlated failures of multiple storage nodes. In particular, this dissertation studies distributed storage systems that can provide resilience against correlated failure patterns that affect the availability of multiple storage nodes, i.e., power loss that may affect multiple disks. Maximum file size that can be stored in such DSS is studied and then optimal code constructions are provided. This dissertation also studies cloud storage systems that prevent data loss from mixed failure patterns of disks and sectors in disk drives. Specifically, a general code construction is proposed to overcome such failures for any given parameter set. Due to its large field size requirement of proposed construction, a relaxation on the efficiency of storage system is considered to provide codes with smaller field sizes. Maintenance of cloud storage systems is also studied. To that end, this dissertation first studies the maintenance of DSS that include a backup node, which is called hierarchical DSS. Hierarchical DSS can model cellular networks such as femtocell as well as caching in wireless networks. In particular, we present an upper bound on the file size that can be stored over hierarchical DSS and propose optimal code constructions. Then, maintenance cost and data access cost for users of such DSS are studied. Lastly, mobility effects of cloud storage over wireless devices are studied. Specifically, an analysis on the mobile cloud storage system that initiates the maintenance process after certain number of devices remains in the network is performed and different maintenance strategies are proposed that are optimal with respect to average cost in certain mobility regimes.
机译:由于数据的不断增长以及最近对存储和分析数据的兴趣,分布式存储系统(DSS)(也称为云存储)已成为文献中最重要的研究领域之一。这样的网络不仅被用作Google,Microsoft和Facebook等公司的骨干系统,而且还加速了云计算的增长,而云计算是IBM,Amazon和Salesforce等机构的重要业务线。在本文中,重点是在云的存储方面,以解决设计此类系统中的重要问题。首先,采用编码理论方法来处理多个存储节点的相关故障。特别地,本论文研究了分布式存储系统,其可以提供对相关故障模式的弹性,该故障模式影响多个存储节点的可用性,即,可能影响多个磁盘的功率损耗。研究了可以存储在此类DSS中的最大文件大小,然后提供了最佳的代码构造。本文还研究了防止由于磁盘和磁盘驱动器中的扇区混合故障模式而导致数据丢失的云存储系统。具体而言,提出了一种通用代码构造来克服对于任何给定参数集的此类故障。由于所提出的结构需要很大的字段大小,因此考虑到存储系统效率的放宽以提供具有较小字段大小的代码。还研究了云存储系统的维护。为此,本文首先研究了包含备份节点的DSS的维护,这称为分层DSS。分层DSS可以对诸如毫微微小区之类的蜂窝网络以及无线网络中的缓存进行建模。特别是,我们提出了可以存储在分层DSS上的文件大小的上限,并提出了最佳的代码构造。然后,研究了这种DSS用户的维护成本和数据访问成本。最后,研究了云存储在无线设备上的移动性影响。具体而言,对在网络中保留了一定数量的设备后启动维护过程的移动云存储系统进行了分析,并提出了不同的维护策略,这些策略对于某些移动方式中的平均成本而言是最佳的。

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    Calis Gokhan;

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  • 年度 2017
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