Collaborative Research: CNS Core: Medium:HardLambda: A new FaaS Abstraction for Cross-Stack Resource Management in Disaggregated Datacenters

数据中心使用的计算机服务器不再能够满足新兴应用程序（如医疗保健、智能基础设施设计和高速物理中的应用程序）的性能和扩展需求。传统设计的服务器的功能与现代应用程序的动态需求之间存在根本的不匹配。这种不匹配导致资源利用率低下和资源浪费严重。一种设计数据中心的新方法，称为分解方法，可以通过创建一个基于需求的按需计算模型来解决这个问题。在这里，服务器被专门用于执行特定的功能，应用程序只使用那些最适合执行每个应用程序所需功能的专用服务器。虽然分解方法提高了利用率并使数据中心更易于管理，但它以性能为代价：分解要求应用程序访问分布在数据中心网络上的一组专用服务器上的关键资源。为了减轻这种资源分解的挑战，本项目设计了HardLambda，这是一种新的功能即服务（FaaS）抽象，它以统一的方式将应用程序的功能和硬件需求结合在一起。HardLambda使数据中心能够以最能满足应用程序需求的方式分配资源，同时保留分解硬件的资源利用率和管理灵活性。设计的算法和系统软件将实现可扩展控制和共享分解资源，并创建自适应资源分配的新方法。HardLambda将使分解数据中心成为科学和工业中众多应用的可行和可持续的选择。该项目特别针对机器和深度学习（ML/DL）应用程序，因为它们在现代计算驱动生活的许多方面发挥着越来越重要的作用。同时，HardLambda将提高大型数据中心的可持续性，其中高利用率、高效率和对应用需求的持续适应都是必不可少的因素。该研究将在硬件和软件共同设计的FaaS系统和服务方面创造新的知识，并为大规模高效地支持ML/DL应用程序提供见解。该项目将与工业界和国家研究实验室的合作伙伴合作，在实际系统中部署HardLambda，并将开展教育和扩大参与活动，以提高社区对大规模计算基础设施的规模和可持续性挑战的认识和理解。将特别强调让代表性不足群体的学生参与研究和教育活动。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。

Datacenters use computer servers that are no longer able to address the performance and scaling demands of emerging applications such as those in healthcare, smart infrastructure design, and high-speed physics. There is a fundamental mismatch between the capabilities of traditionally designed servers and the dynamic requirements of modern applications. This mismatch leads to poor utilization and significant waste of resources. A new way to design datacenters, called the disaggregated approach, can address this problem by creating a need-based on-demand model for computing. Here, servers are specialized to perform specific functions, and applications use only those specialized servers that best perform the functions needed by each application. While the disaggregated approach improves utilization and makes datacenters easier to manage, it comes at a performance cost: disaggregation requires applications to access critical resources spread across a set of specialized servers over the datacenter network. To mitigate such challenges of resource disaggregation, this project designs HardLambda, a new Function-as-a-Service (FaaS) abstraction that brings the functional and hardware requirements of an application together in a unified fashion. HardLambda enables datacenters to allocate resources in ways that best meet application needs while retaining the resource utilization and management flexibility of disaggregated hardware. The designed algorithms and system software will enable scalable control and sharing of disaggregated resources, and create new approaches to adaptive resource allocation. HardLambda will make disaggregated datacenters a viable and sustainable option for numerous applications in science and industry. The project especially targets machine and deep learning (ML/DL) applications due to their increasingly crucial role in many aspects of modern computing-powered life. At the same time, HardLambda will improve the sustainability of large-scale datacenters, where high utilization, efficiency, and continuous adaptation to application requirements are all essential factors. The research will create new knowledge on hardware and software co-designed FaaS systems and services, and yield insights for efficiently supporting ML/DL applications at extremely large scales. The project will engage with partners in industry and national research laboratories to deploy HardLambda in real systems and will undertake educational and broadening participation activities to improve community awareness and understanding of the scaling and sustainability challenges of large-scale computing infrastructure. Special emphasis will be given to engaging students from underrepresented groups in the research and educational activities.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.