CNS Core: Small: Operating Systems Abstractions for Serverless Computing

无服务器功能，或功能即服务(FAAS)，是一种云计算功能，近年来越来越受欢迎。该项目将通过复杂的运行时系统改进无服务器功能，该系统将允许用户高效运行代码，同时保持无服务器功能对提供商来说在经济上可行。在保持编程模型简单的同时，更复杂的运行时将提供有效的中间结果缓存和容错等功能。同时，硬件加速(例如，图形处理单元(GPU))将被透明地启用。因此，对于视频处理和机器学习推理等新的工作负载，无服务器功能将变得高效。使用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关系，而运行时同时调度本地存储和计算加速器以及更传统的资源，如中央处理单元(CPU)内核和内存。无服务器工作负载需要高并行度和较短的运行时间，才能使平台物有所值。然而，由于依赖输入的处理要求和GPU加速，维持高水平的并行度可能很困难。当数据流图中指定的阶段具有依赖于数据的处理要求时，就会出现负载不平衡。这在一些与机器学习(ML)相关的任务中很常见，例如人脸识别。GPU可能会使问题变得更糟，因为在执行速度更快的GPU上执行某些阶段时，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，以前所未有的性能为新工作负载带来无服务器功能。这使得依赖机器学习和其他计算密集型计算的系统能够继续进行指数级的进化和创新。这个项目还将为博士生提供一个担任研究生研究助理的机会，同时获得广泛的接触跨学科研究的机会，这些研究来自计算机科学的多个领域，包括操作系统、虚拟化和GPU。该项目的结果将在可以存档的地方公开。来自该项目的所有出版材料将在作者的网站上免费分发。研究构件可能包括修改后的源代码和工作负载。研究出版物将在https://www.cs.utexas.edu/users/witchel/.上获得源代码、工作负载和其他文物将在https://github.com/ut-osa/.This网站上提供，该奖项反映了美国国家科学基金会的法定使命，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。

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.