CNS Core: Small: Operating Systems Abstractions for Serverless Computing

CNS 核心：小型：无服务器计算的操作系统抽象

• 批准号: 2008321
• 负责人: Emmett Witchel
• 金额: $ 50万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008321&HistoricalAwards=false
• 关键词: CNS; Core; Small; Operating; Systems

Serverless functions, or Functions as a Service (FaaS), are a cloud computing feature whose popularity has been increasing in recent years. This project will improve serverless functions with a sophisticated runtime system that will allow users to run code efficiently while keeping serverless functions economically viable to providers. While keeping the programming model simple, a more sophisticated runtime will provide features such as efficient caching of intermediate results and fault tolerance. Meanwhile hardware acceleration (e.g., graphical processing units (GPUs)) will be transparently enabled. As a consequence, serverless functions will be made efficient for new classes of workloads such as video processing and machine learning inference.Achieving efficient execution with a simple programming model requires a technically sophisticated runtime system. Organizing the computation as a data flow graph allows the user to provide only simple data dependencies while the runtime simultaneously schedules local storage and computational accelerators along with more traditional resources such as the Central Processing Unit (CPU) cores and memory. Serverless workloads require high parallelism and short run times to make the platform worthwhile. However, maintaining high levels of parallelism can be difficult because of input-dependent processing requirements and GPU acceleration. Load imbalance arises when the stages specified in a data flow graph have data-dependent processing requirements. This is common in some machine learning (ML) related tasks, e.g., face recognition. GPUs may make the problem worse because a data flow graph that is balanced for CPU execution might become unbalanced when some stages are executed on a GPU where execution is much faster.This project will provide the necessary tools, techniques, and infrastructure to bring serverless functions to new workloads with unprecedented levels of performance. This allows the continued exponential evolution and innovation for systems that rely on machine learning and other compute-intensive computations. This project will also provide an opportunity for doctoral students to work as graduate research assistants while gaining broad exposure to interdisciplinary research that draws from multiple areas of computer science, including operating systems, virtualization and GPUs.Results from this project will be made public where they can be archived. All published material from the project will be distributed for free from the authors' web site. Research artifacts are likely to include modified source code and workloads. Research publications will be available at https://www.cs.utexas.edu/users/witchel/. Source code, workloads, and other artifacts will be available at https://github.com/ut-osa/.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.

无服务器功能或功能即服务（FaaS）是近年来越来越受欢迎的云计算功能。该项目将通过一个复杂的运行时系统来改进无服务器功能，该系统将允许用户有效地运行代码，同时保持无服务器功能对提供商的经济可行性。 在保持编程模型简单的同时，更复杂的运行时将提供诸如中间结果的高效缓存和容错等特性。 同时硬件加速（例如，图形处理单元（GPU））将被透明地启用。 因此，无服务器功能将能够高效地处理视频处理和机器学习推理等新类型的工作负载。通过简单的编程模型实现高效执行需要技术复杂的运行时系统。 将计算组织为数据流图允许用户仅提供简单的数据依赖性，而运行时同时调度本地存储和计算加速器沿着更传统的资源，诸如中央处理单元（CPU）核和存储器。 无服务器工作负载需要高并行性和短运行时间，以使平台物有所值。 然而，由于依赖于输入的处理要求和GPU加速，维持高水平的并行性可能是困难的。 当数据流图中指定的阶段具有依赖于数据的处理需求时，就会出现负载不平衡。 这在一些机器学习（ML）相关任务中很常见，例如，人脸识别GPU可能会使问题变得更糟，因为当某些阶段在执行速度更快的GPU上执行时，为CPU执行平衡的数据流图可能会变得不平衡。该项目将提供必要的工具，技术和基础设施，以前所未有的性能水平将无服务器功能带入新的工作负载。 这使得依赖机器学习和其他计算密集型计算的系统能够持续指数级进化和创新。该项目还将为博士生提供担任研究生研究助理的机会，同时获得广泛接触计算机科学多个领域的跨学科研究的机会，包括操作系统，虚拟化和GPU。该项目的结果将公开，可以存档。 该项目的所有出版材料将从作者的网站上免费分发。 研究工件可能包括修改的源代码和工作负载。 研究出版物可在https://www.cs.utexas.edu/users/witchel/上查阅。 源代码、工作负载和其他工件将在www.example.com上提供https://github.com/ut-osa/.This奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Nightcore: efficient and scalable serverless computing for latency-sensitive, interactive microservices

• DOI: 10.1145/3445814.3446701
• 发表时间: 2021-04
• 期刊: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems
• 作者: Zhipeng Jia;Emmett Witchel

Boki: Stateful Serverless Computing with Shared Logs

Boki：具有共享日志的状态无服务器计算

• DOI: 10.1145/3477132.3483541
• 发表时间: 2021
• 期刊: Proceedings of the ACM SIGOPS 28th Symposium on Operating Systems Principles
• 作者: Jia, Zhipeng;Witchel, Emmett
• 通讯作者: Witchel, Emmett