迈向 Tapis 中的智能调度
Toward Smart Scheduling in Tapis
AI总结：
本文研究 Tapis 框架中的智能作业调度，提出总体架构并聚焦排队时间预测，分别用回归和分类方法实现系统选择与动态资源配置比较。
摘要：
Tapis 框架提供了一系列 API，用于在远程资源（包括 HPC 集群和运行在云端的服务器）上自动化作业执行。Tapis 可以简化与远程网络基础设施（CI）的交互，但当前服务要求用户指定要运行作业的确切配置，包括系统、队列、节点数量和最大运行时间等属性。此外，在提交作业之前，远程资源必须在 Tapis 中完成定义和配置。在本文中，我们介绍了在 Tapis 中开发智能作业调度能力的工作，其中作业配置的各种属性可以为用户自动确定，并且计算资源可以由 Tapis 针对特定作业动态配置。我们为这一功能开发了一个总体架构，该架构提出了一组需要解决的核心挑战。然后，我们聚焦于其中一个具体挑战：预测作业在不同 HPC 系统和队列上的排队时间，并基于机器学习方法给出了两组结果。我们的第一组结果将该问题建模为回归问题，可用于从现有选项列表中选择最佳系统。我们的第二组结果将该问题建模为分类问题，使我们能够比较使用现有系统与动态配置资源的情况。
英文摘要：
The Tapis framework provides APIs for automating job execution on remote resources, including HPC clusters and servers running in the cloud. Tapis can simplify the interaction with remote cyberinfrastructure (CI), but the current services require users to specify the exact configuration of a job to run, including the system, queue, node count, and maximum run time, among other attributes. Moreover, the remote resources must be defined and configured in Tapis before a job can be submitted. In this paper, we present our efforts to develop an intelligent job scheduling capability in Tapis, where various attributes about a job configuration can be automatically determined for the user, and computational resources can be dynamically provisioned by Tapis for specific jobs. We develop an overall architecture for such a feature, which suggests a set of core challenges to be solved. Then, we focus on one such specific challenge: predicting queue times for a job on different HPC systems and queues, and we present two sets of results based on machine learning methods. Our first set of results cast the problem as a regression, which can be used to select the best system from a list of existing options. Our second set of results frames the problem as a classification, allowing us to compare the use of an existing system with a dynamically provisioned resource.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
