CSR: Small: Cost Effective, High Performance Solutions Using Erasure Codes for Big Data Management in Large Data Centers

CSR：小型：在大型数据中心使用纠删码进行大数据管理的经济高效、高性能解决方案

• 批准号: 1700719
• 负责人: Xubin He
• 金额: $ 7.11万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1700719&HistoricalAwards=false
• 关键词: CSR; Small; Cost; Effective; Performance; CSR; Small; Cost; Effective; Performance

Data and I/O availability is an increasing concern in todayÕs large data centers where both data volume and complexity are increasing dramatically. Most existing solutions are based on multi-replication techniques to provide data redundancy, where data chunks are replicated across storage server nodes. However, multi-replication techniques are insufficient to manage big data: itÕs a big challenge to efficiently replicate N copies of a data set of tens-to-hundreds of petabytes! As an alternative solution, erasure codes tolerating multiple failures can provide reliability and availability at much lower cost. However, the biggest challenge using erasure codes to manage big data is the performance problem due to the complex encoding/decoding operations, which limits the application of erasure codes in large-scale data centers. This project develops cost effective techniques to exploit erasure codes to achieve high availability and enhance performance in large data centers to efficiently manage big data via several research innovations. This project cohesively investigates how to utilize proper spatial cost and system/architecture techniques to improve the overall data access performance of server clusters built upon erasure codes. This research has fundamental contributions to pave the way to efficiently deploy data centers using erasure codes. It has potential to benefit numerous big data applications such as online searching, social network, e-business, health care, and so on which are typically data intensive.

数据和I/O的可用性在当今的大数据中心越来越多，因为数据量和复杂性都在急剧增加。大多数现有的解决方案基于多复制技术，以提供数据冗余，其中数据块在存储服务器节点上复制。但是，多复制技术不足以管理大数据：有效复制数十百pb体的数据集的n副本是一个巨大的挑战！作为替代解决方案，可容忍多个故障的擦除代码可以以更低的成本提供可靠性和可用性。但是，使用Erasure代码来管理大数据的最大挑战是由于复杂的编码/解码操作而导致的性能问题，这限制了在大规模数据中心中的擦除代码的应用。该项目开发了具有成本效益的技术来利用擦除代码，以实现高可用性并提高大型数据中心的性能，以通过几项研究创新有效地管理大数据。该项目凝聚力研究了如何利用适当的空间成本和系统/体系结构技术来改善基于擦除代码的服务器群集的整体数据访问性能。这项研究为使用擦除代码有效部署数据中心的方式铺平了基本贡献。它有可能受益于许多大数据应用程序，例如在线搜索，社交网络，电子商务，医疗保健等，这些应用程序通常是数据密集型的。