SI2-SSE: AMASS - An Automated Monitoring AnalySis Service for Cyberinfrastructure

科学网关是由社区开发的一组工具、应用程序和数据集合，通过门户或一套应用程序进行集成。它提供了对超级计算机、软件工具和数据存储库的简单访问，通常是基于浏览器的访问，使研究人员能够专注于他们的科学目标，而不是网络基础设施。这些门户正在促进来自大气科学、天体物理学、化学、生物物理学、生物化学、地震工程、生物物理学、神经科学和生物学等多个社区的数千名研究人员之间的合作和思想交流。然而，由于有限的开发和管理人员资源，科学网关通常只利用NSF资助的CI的一小部分来减轻部分由于软件和硬件故障而大规模使用多个资源和服务所涉及的复杂性。由于许多成功的科学网关在其用户群和不断增加的数据集方面都有前所未有的增长，因此在不引入额外复杂性的情况下增加其CI资源的使用将有助于满足这一需求。为了响应这一需求，将建立自动监测分析服务（AMASS），以提供灵活和可扩展的服务，用于对最初集中在科学网关上的监测数据进行自动分析。 AMASS将基于数据挖掘和机器学习技术以及新兴的大数据技术来分析监测数据，以提高CI的可靠性和运营效率，以及在系统和种群生物学，计算神经科学和生物物理学社区的基本问题上取得进展。 沿着AMASS，将建立一个模拟框架，用于测试自动分析算法和自适应执行技术。 将为科学网关软件提供直观的查询API，并将其集成到以下三个目标科学网关中，以推动项目的研究和开发：系统发育研究网络基础设施（CIPRES），神经科学网关（NSG）和UltraScan。所提出的方法不需要对最终用户应用程序进行任何更改，软件开发将通过将监测数据集成到其基础设施中以实现应用程序的自适应执行，从而显着提高科学网关的科学生产力和用户满意度，使科学家能够回答更复杂的问题，而不必了解大规模分布式环境的复杂性。开发的软件产品将作为Apache许可证下的开源产品提供，并将集成到NSF资助的SciGap项目中，以影响更广泛的科学网关。

A science gateway is a community-developed set of tools, applications, and data collections that are integrated via a portal or a suite of applications. It provides easy, typically browser-based, access to supercomputers, software tools, and data repositories to allow researchers to focus on their scientific goals and less on the cyberinfrastructure. These gateways are fostering collaboration and exchange of ideas among thousands of researchers from multiple communities ranging from atmospheric science, astrophysics, chemistry, biophysics, biochemistry, earthquake engineering, geophysics, to neuroscience, and biology. However due to limited development and administrative personnel resources, science gateways often leverage only a small subset of the NSF-funded CI to mitigate the complexities involved with using multiple resource and services at scale in part due to software and hardware failures. Since many successful science gateways have had unprecedented growth in their user base and ever increasing datasets, increasing their usage of CI resources without introducing additional complexity would help them meet this demand.In response to this need, an Automated Monitoring AnalySis Service (AMASS) will be built to provide a flexible and extensible service for automated analysis of monitoring data initially focused on science gateways. AMASS will be based on data mining and machine learning techniques and emerging big data technologies to analyze monitoring data for improving the reliability and operational efficiency of CI as well as progress on fundamental questions in systematic and population biology, computational neuroscience, and biophysics communities. Along with AMASS, a simulation framework will be built for testing automated analysis algorithms and adaptive execution techniques. An intuitive query API will be provided for science gateway software to use and will be integrated into the following three target science gateways that will drive the project's research and development: the Cyberinfrastructure for Phylogenetic Research (CIPRES), the Neuroscience Gateway (NSG), and UltraScan. The proposed approach does not require any changes to the end user applications, and the software developments will significantly enhance the science productivity and user satisfaction of science gateways by integrating monitoring data into their infrastructure to enable adaptive execution of their applications, allowing scientists to answer more sophisticated questions without having to understand the complexities of a large-scale distributed environment. The developed software products will be available as open source products under an Apache License and will be integrated into the NSF-funded SciGap project in order to impact a broader range of science gateways.