CICI: SSC: Integrity Introspection for Scientific Workflows (IRIS)

科学家使用计算机系统来分析和存储他们的科学数据，有时会在不同地理位置的多台机器上进行复杂的处理。据观察，有时在这一复杂的过程中，科学数据被无意中修改或意外篡改，错误未被发现，损坏的数据成为科学记录的一部分。IRIS项目解决了在科学处理工作流程中可能出现的这些无意数据错误的检测和诊断问题。这种方法是从处理过程中涉及的计算和网络系统的各个部分收集与科学数据的正确性和完整性相关的数据，并使用机器学习技术分析收集的数据，以发现科学数据处理中的错误。这些解决方案被集成到Pegasus中，这是一个流行的“工作流管理系统”--一种软件，用于以用户友好的方式描述复杂的过程，并为科学家处理处理的细节。这些研究方法将在国家计算资源上进行验证，并以引力波物理、地震科学和生物信息学的范例科学应用为例。这些解决方案将使科学家和我们的社会对基于收集的数据的科学发现更有信心。数据驱动的科学工作流在分布式国家网络基础设施(CI)上执行时往往会出现无意的数据完整性错误。然而，今天，缺乏能够从工作流中收集和分析与完整性相关的数据的工具，因此，许多这些错误都没有被发现，从而危及科学结果的有效性。IRIS项目的目标是自动检测、诊断和查明在分布式CI上执行的科学工作流程中意外完整性异常的来源。方法是开发适当的威胁模型，并将其合并到完整性分析框架中，该框架收集工作流和基础设施数据，并使用机器学习(ML)算法执行所需的分析。该框架由基于ML的新方法提供支持，这些方法是通过在受控试验台上的实验开发的，并在NSF生产CI中得到验证和广泛使用。这些解决方案将被集成到飞马工作流管理系统中，该系统被广泛的科学领域使用。该项目的一个重要部分是与引力波物理、地震科学和生物信息学领域的科学应用合作伙伴合作，为他们的工作流程部署分析框架，并在反馈循环中反复微调威胁模型、ML模型培训和ML模型验证。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Scientists use computer systems to analyze and store their scientific data, sometimes in a complex process across multiple machines in different geographical locations. It has been observed that sometimes during this complex process, scientific data is unintentionally modified or accidentally tampered with, with errors going undetected and corrupt data becoming part of the scientific record. The IRIS project tackles the problem of detecting and diagnosing these unintentional data errors that might occur during the scientific processing workflow. The approach is to collect data relevant to the correctness and integrity of the scientific data from various parts of the computing and network system involved in the processing, and to analyze the collected data using machine learning techniques to uncover errors in the scientific data processing. The solutions are integrated into Pegasus, a popular "workflow management system" - a software used to describe the complex process in a user-friendly way and that handles the details of processing for the scientists. The research methods will be validated on national computing resources with exemplar scientific applications from gravitational-wave physics, earthquake science, and bioinformatics. These solutions will allow scientists, and our society, to be more confident of scientific findings based on collected data.Data-driven science workflows often suffer from unintentional data integrity errors when executing on distributed national cyberinfrastructure (CI). However, today, there is a lack of tools that can collect and analyze integrity-relevant data from workflows and thus, many of these errors go undetected jeopardizing the validity of scientific results. The goal of the IRIS project is to automatically detect, diagnose, and pinpoint the source of unintentional integrity anomalies in scientific workflows executing on distributed CI. The approach is to develop an appropriate threat model and incorporate it in an integrity analysis framework that collects workflow and infrastructure data and uses machine learning (ML) algorithms to perform the needed analysis. The framework is powered by novel ML-based methods developed through experimentation in a controlled testbed and validated in and made broadly available on NSF production CI. The solutions will be integrated into the Pegasus workflow management system, which is used by a wide variety of scientific domains. An important part of the project is the engagement with science application partners in gravitational-wave physics, earthquake science, and bioinformatics to deploy the analysis framework for their workflows, and to iteratively fine tune the threat models, ML model training, and ML model validation in a feedback loop.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.