The Large Hadron Collider (LHC) at CERN has generated in the last decade an unprecedented volume of data for the High-Energy Physics (HEP) field. Scientific collaborations interested in analysing such data very often require computing power beyond a single machine. This issue has been tackled traditionally by running analyses in distributed environments using stateful, managed batch computing systems. While this approach has been effective so far, current estimates for future computing needs of the field present large scaling challenges. Such a managed approach may not be the only viable way to tackle them and an interesting alternative could be provided by serverless architectures, to enable an even larger scaling potential. This work describes a novel approach to running real HEP scientific applications through a distributed serverless computing engine. The engine is built upon ROOT, a well-established HEP data analysis software, and distributes its computations to a large pool of concurrent executions on Amazon Web Services Lambda Serverless Platform. Thanks to the developed tool, physicists are able to access datasets stored at CERN (also those that are under restricted access policies) and process it on remote infrastructures outside of their typical environment. The analysis of the serverless functions is monitored at runtime to gather performance metrics, both for data- and computation-intensive workloads.
翻译:近十年来,欧洲核子研究中心大型哈德龙对撞机(LHC)为高能物理(HEP)领域生成了前所未有的数据数量。科学合作分析这类数据往往需要超过一台机器的计算能力。这个问题传统上是通过使用有条不紊管理的批量计算系统对分布式环境进行分析来解决的。虽然这种方法到目前为止是有效的,但目前对外地未来计算需求的估算却带来了巨大的规模化挑战。这种管理方法可能不是解决这些问题的唯一可行方法,而一种有趣的替代方法可能是无服务器建筑,能够提供更大的扩展潜力。这项工作描述了通过分布式无服务器计算引擎运行真正的HEP科学应用的新办法。发动机建在ROOT上,这是一套完善的HEP数据分析软件,在亚马逊网络服务Lamba服务器服务器平台上将其计算结果传播到大量同时处决。由于开发的工具,物理学家能够访问欧洲核子研究中心储存的数据集(也处于有限访问政策之下),并在其典型环境以外的远程基础设施上进行处理。对无服务器功能的分析是在正常环境中进行运行的运行量量度计算。