SHF:AF:Small:Collaborative Research:RESAR: Robust, Efficient, Scalable, Autonomous Reliable Storage for the Cloud

SHF:AF:Small:协作研究：RESAR：稳健、高效、可扩展、自主可靠的云存储

• 批准号: 1219163
• 负责人: Darrell Long
• 金额: $ 37.47万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1219163&HistoricalAwards=false
• 关键词: SHF; AF; Small; Collaborative; Research

With the growth of cloud computing and the changing manner in which individuals and businesses interact with data, it is increasingly important to manage data efficiently and reliably. The RESAR project tackles the problem of building ever-larger data stores, and offers a novel approach to reducing the energy impact of such increases in scale while allowing easier management and adaptation of the system as it ages. In other words, RESAR offers a means to gracefully adapt a storage system to offer increased reliability or performance as demanded by the systems' age or administrator's requirements. This project develops, studies, and optimizes reliable, energy-efficient storage needed in modern data centers and large-scale data storage environments, and allows such storage systems to gracefully increase its performance and reliability while efficiently scaling to millions of storage devices.For storage systems to be feasible and manageable at increasing scales, need to be self-healing and self-optimizing, able to adapt to aging and new components whilst dynamically recovering from inevitable component failures. Cloud computing promises savings in staffing as the volume of work in a data center would be distributed over fewer, but better trained staff. While the increasing scale of such data centers offers greater opportunities for energy-saving measures to become more effective, such scales rapidly increase fears of individual components failing. This demands that such large scale storage systems be arranged in such a way as to offer an ability to survive the failure of multiple components, and to do so with minimal management overheads.To survive the increasingly likely component failures (brought about by the increasing numbers of components in ever-growing data warehouses), storage systems typically employ some form of data replication or redundancy scheme. This strategy not only protects data against loss, but also allows faster access. Unfortunately, doubling or tripling the number of storage devices (or entire data centers) comes at a considerable cost. Alternatively, a site could use erasure correcting codes that provide protection against device failures while only increasing hardware demands by a smaller increment. But such erasure correcting schemes offer limited scalability and can complicate the implementation and self-management of a system considerably. The RESAR approach is to employ novel erasure codes that allow faster layout restructuring, while offering increased scalability, and improved reliability over competing schemes. RESAR allows for restructuring on the fly, and as such, has the added benefit of being complementary to data relocation tasks necessary for routine maintenance and optimization.Cloud computing and data centers are taking hold as technologies with great promise for cheaper, more flexible, and more energy-efficient information processing. RESAR enables cheaper, more reliable, automated and more easily scaled storage systems. RESAR offers a novel graph representation of a failure tolerance scheme that allows the construction of flexible, dynamically reconfigurable, parity-based redundancy schemes that are well-suited for cloud storage infrastructure. By offering the benefits of more highly-convolved erasure coding schemes, whilst remaining simple and efficient, RESAR offers a new path to self-organizing large-scale storage systems. The resulting systems are more maintainable, easily reconfigured for increasing levels of reliability on-demand, and more cost effective. This efficiency further extends to reduced maintenance and energy demands.

随着云计算的增长以及个人和企业与数据互动的变化方式，有效，可靠地管理数据越来越重要。 RESAR项目解决了建立越来越多的数据存储的问题，并提供了一种新颖的方法来减少这种增加的能量影响，同时可以随着年龄的增长而更容易地管理和适应系统。换句话说，Resar提供了一种优雅地适应存储系统的方法，以根据系统年龄或管理员的要求提供更高的可靠性或性能。该项目在现代数据中心和大规模数据存储环境中开发，研究并优化了可靠，节能的存储，并允许此类存储系统优雅地提高其性能和可靠性，同时有效地扩展到数百万个存储设备。要使存储系统可容易且可在尺度上可容易地进行，并且需要自动化，并适应自动化，并在适应性上恢复，并在适应性上恢复了新的综合，以及新的综合量失败。云计算有望节省人员，因为数据中心的工作量将以更少但培训更好的员工分发。尽管此类数据中心的规模不断增加为节能措施变得更加有效提供了更多的机会，但此类量表迅速增加了对单个组件失败的恐惧。这要求将如此大规模的存储系统安排以提供在多个组件失败的能力上的能力，并在最小的管理开销中这样做。为了生存越来越可能的组件故障（由于越来越多的成长数据备件中的组件越来越多），存储系统通常会采用某种形式的数据复制或重复的计划。该策略不仅可以保护数据免受损失，还可以更快地访问。不幸的是，将存储设备数量（或整个数据中心）的数量增加一倍或三倍，这是相当大的成本。另外，站点可以使用擦除校正代码，该代码可为设备故障提供保护，同时仅增加硬件需求的增加。但是，这种擦除校正方案提供了有限的可扩展性，并使系统的实施和自我管理变得复杂。 Resar方法是采用新颖的擦除代码，以更快的布局重组，同时提供可扩展性的提高，并提高了对竞争方案的可靠性。 Resar允许即时进行重组，因此，具有对日常维护和优化所需的数据搬迁任务的补充。CloudComputing和Data Centers作为具有巨大希望，更便宜，更灵活且能富有能力效率的信息处理的技术。 Resar启用更便宜，更可靠，自动化和更容易缩放的存储系统。 RESAR提供了一种新颖的图表表示故障公差方案，该方案允许构建灵活的，动态的可重新配置，基于平价的冗余方案，非常适合用于云存储基础架构。通过提供更高度互动的擦除编码方案的好处，同时保持简单和高效，Resar为自组织大规模存储系统提供了新的途径。所得系统更可维护，易于重新配置，以提高按需的可靠性水平，并且更具成本效益。这种效率进一步扩展到减少的维护和能源需求。