New Approaches to Multiserver Scheduling

该奖项将通过获得有效的调度方法来改善一系列关键服务行业的响应时间，特别是在云计算和数据中心管理方面，为促进国家繁荣和经济福利做出贡献。 人工智能应用的显著增长，特别是大型语言模型，给现有的计算环境带来了沉重的负担，需要高效调度数千个处理资源来满足性能要求。 该项目将开发新的嵌入式方法，以超越当前的单服务器或小型多服务器模型，扩展到非常大规模的多服务器模型。 该项目将提供工具，使从业人员能够设计更智能的调度策略，从而提高现代数据中心的响应能力和效率，并带来经济和环境效益。 该项目包括指导女研究生的活动，并通过教程和在线教科书向不同社区广泛传播成果，该项目将采用调度理论分析来探索复杂调度政策的性能，包括最短剩余处理时间和Gittins指数，用于多服务器队列。 该项目将为多服务器系统的调度开发新的理论，大大扩展了以前考虑的作业模型，并允许对当今数据中心进行更具代表性的表征。 这些包括考虑多服务器作业，其中单个作业需要多个服务器同时运行，作业动态变化的持有成本，以及作业之间的依赖关系。 考虑的性能指标也将由当今的云系统驱动，特别强调满足尾部响应时间目标，而不仅仅是满足平均等待时间目标。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award will contribute to the advancement of national prosperity and economic welfare by deriving efficient scheduling methods to improve response times across a range of critical service industries, notably in cloud computing and data center management. The significant rise in artificial intelligence applications, particularly large language models, puts a heavy burden on existing computing environments and requires efficient scheduling of thousands of processing resources to meet performance requirements. This project will develop new queueing methods to extend beyond current single- or small multi-server models to very large scale multi-server models. The project will provide tools that enable practitioners to design smarter scheduling policies, thereby improving the responsiveness and efficiency of modern data centers and resulting in economic and environmental benefits. The project includes activities to mentor female graduate students, and broad dissemination of results to different communities through tutorials and an online textbook.This project will employ queueing-theoretic analysis to explore the performance of complex scheduling policies, including Shortest Remaining Processing Time and the Gittins Index, for multi-server queues. The project will develop new theory for scheduling in multi-server systems that significantly extends previously considered job models and allows for far more representative characterization of today's data centers. These include consideration of multi-server jobs, where a single job requires several servers simultaneously to run, jobs with dynamically changing holding costs, and dependencies between jobs. The performance metrics considered will also be driven by today's cloud systems, with particular emphasis on meeting tail response time targets, rather than simply meeting average waiting time targets.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.