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

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

• 批准号: 1217648
• 负责人: Ahmed Amer
• 金额: $ 11万
• 依托单位: Santa Clara University
• 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 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217648&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.

随着云计算的发展以及个人和企业与数据交互方式的不断变化，高效可靠地管理数据变得越来越重要。RESAR项目解决了构建越来越大的数据存储的问题，并提供了一种新的方法来减少这种规模增加的能源影响，同时允许随着系统的老化而更容易地管理和适应。换句话说，RESAR提供了一种方法来优雅地调整存储系统，以根据系统的年龄或管理员的要求提供更高的可靠性或性能。该项目开发、研究和优化现代数据中心和大规模数据存储环境所需的可靠、节能的存储，并允许此类存储系统在有效扩展到数百万个存储设备的同时优雅地提高其性能和可靠性。能够适应老化和新组件，同时从不可避免的组件故障中动态恢复。云计算承诺节省人员配置，因为数据中心的工作量将分配给更少但受过更好培训的员工。虽然这些数据中心的规模不断扩大，为节能措施变得更加有效提供了更多机会，但这种规模迅速增加了对单个组件故障的担忧。这就要求这样的大规模存储系统以这样的方式布置，即提供在多个组件的故障下生存的能力，并且以最小的管理开销来做到这一点。为了在越来越可能的组件故障（由不断增长的数据仓库中的组件数量的增加所带来的）下生存，存储系统通常采用某种形式的数据复制或冗余方案。这种策略不仅可以防止数据丢失，还可以更快地访问。不幸的是，将存储设备（或整个数据中心）的数量增加一倍或两倍需要相当大的成本。或者，站点可以使用擦除校正码来提供针对设备故障的保护，同时仅以较小的增量增加硬件需求。但是这种擦除校正方案提供有限的可扩展性，并且可能使系统的实现和自我管理相当复杂。RESAR方法是采用新的擦除码，允许更快的布局重组，同时提供更高的可扩展性，并提高可靠性的竞争方案。RESAR允许动态重组，因此，具有补充日常维护和优化所需的数据迁移任务的额外好处。云计算和数据中心正在成为具有更便宜，更灵活，更节能的信息处理的巨大前景的技术。RESAR使存储系统更便宜、更可靠、自动化和更容易扩展。RESAR提供了一种新的容错方案的图形表示，允许构建灵活的，动态可重构的，基于奇偶校验的冗余方案，非常适合云存储基础设施。通过提供更多高度卷积的擦除编码方案的好处，同时保持简单和高效，RESAR提供了一条新的自组织大规模存储系统的道路。由此产生的系统更易于维护，易于重新配置，以提高按需可靠性水平，并且更具成本效益。这种效率进一步扩展到减少维护和能源需求。