ATLAS和CMS实验中大数据处理的进展
Advancements in Big Data Processing in the ATLAS and CMS Experiments
摘要：
不断增长的科学数据量对分布式计算和网格技术提出了新的挑战。新兴的大数据革命推动了科学领域的探索，包括纳米技术，天体物理学，高能物理学，生物学和医学。新的举措正在改变数据驱动的科学领域，以新的方式进行大规模数据分析。在千万亿次数据处理中，科学家处理的是数据集，而不是单个文件。因此，任务（由许多作业组成）成为网格上千万亿次数据处理的一个单元。将大型数据处理任务拆分为作业可以实现细粒度检查点，类似于在数据传输期间将大型文件拆分为较小的TCP/IP数据包。通过自动重新发送丢弃的TCP/IP数据包，在小数据包中传输大数据可以实现可靠性。类似地，网格上的瞬时作业故障可以通过自动重试来恢复，以在网格上的千万亿次数据处理中实现可靠的六西格玛生产质量。ATLAS和CMS实验的计算经验为可靠性工程提供了基础，将网格技术扩展到千万亿次以上的数据处理。
英文摘要：
The ever-increasing volumes of scientific data present new challenges for distributed computing and Grid technologies. The emerging Big Data revolution drives exploration in scientific fields including nanotechnology, astrophysics, high-energy physics, biology and medicine. New initiatives are transforming data-driven scientific fields enabling massive data analysis in new ways. In petascale data processing scientists deal with datasets, not individual files. As a result, a task (comprised of many jobs) became a unit of petascale data processing on the Grid. Splitting of a large data processing task into jobs enabled fine-granularity checkpointing analogous to the splitting of a large file into smaller TCP/IP packets during data transfers. Transferring large data in small packets achieves reliability through automatic re-sending of the dropped TCP/IP packets. Similarly, transient job failures on the Grid can be recovered by automatic re-tries to achieve reliable six sigma production quality in petascale data processing on the Grid. The computing experience of the ATLAS and CMS experiments provides foundation for reliability engineering scaling up Grid technologies for data processing beyond the petascale.
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