CSR: Small: Collaborative Research: Comprehensive Algorithmic Resilience (CAR) for Big Data Analytics

CSR：小型：协作研究：大数据分析的综合算法弹性 (CAR)

• 批准号: 1527051
• 负责人: Nian-Feng Tzeng
• 金额: $ 25万
• 依托单位: University of Louisiana at Lafayette
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527051&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; Comprehensive

Big Data analytics is the process of mining useful knowledge in very large data sets, critical to the advancement of many research and application fields. With manufacturing technology downscaling coupled with increasing power densities, modern computer systems suffer from potential hardware and software failures, which can manifest themselves as errors. The errors are to happen when long-running Big Data analytics are executed on the systems, causing crash or worse, returning incorrect results silently. While numerous hardware-based resilience methods exist, they often come at the cost of excessive power efficiency reduction and substantial design complexity enlargement among others. The project aims to reinforce popular Big Data analytics by embracing a host of comprehensive algorithmic resilience (CAR) software techniques that include concurrent error detection, coordinated checkpointing, and execution recovery, for high execution resilience. Upon detecting potential hardware and software errors concurrently during analytics, CAR enables execution recovery from detected errors without lofty overhead common to hardware-based resilience methods. Research activities of the project aim to achieve six main objectives that focus on addressing several technical challenges to realize CAR, based on investigators? encouraging preliminary results and prior work. The success of this project can benefit wide scientific and industrial applications due to its better support of Big Data analytics and processing. Research advances from this research are to be incorporated into undergraduate and graduate education, to be disseminated and shared broadly through technical presentations and by a website, and to inspire high school students for their STEM interest.

大数据分析是在非常大的数据集中挖掘有用知识的过程，对许多研究和应用领域的进步至关重要。随着制造技术的缩小以及功率密度的增加，现代计算机系统遭受潜在的硬件和软件故障，这些故障可以表现为错误。这些错误是在系统上执行长时间运行的大数据分析时发生的，导致崩溃或更糟，默默地返回不正确的结果。虽然存在许多基于硬件的弹性方法，但它们通常以过度的功率效率降低和显著的设计复杂性扩大等为代价。 该项目旨在通过采用一系列全面的算法弹性（CAR）软件技术来加强流行的大数据分析，包括并发错误检测，协调检查点和执行恢复，以实现高执行弹性。 在分析过程中同时检测到潜在的硬件和软件错误后，CAR可以从检测到的错误中恢复执行，而不会产生基于硬件的弹性方法常见的高昂开销。 该项目的研究活动旨在实现六个主要目标，重点是解决几个技术挑战，实现汽车，根据调查？鼓励初步成果和前期工作。 该项目的成功可以使广泛的科学和工业应用受益，因为它更好地支持大数据分析和处理。 这项研究的研究进展将被纳入本科和研究生教育，通过技术演示和网站广泛传播和分享，并激发高中学生对STEM的兴趣。