Collaborative Research: PPoSS: Planning: Scaling Secure Serverless Computing on Hetergeneous Datacenters

云计算一直是占主导地位的计算模式，支持许多重要的计算能力，包括大规模(大数据)处理、人工智能和科学发现。云计算的最新发展包括转向无服务器计算，这简化了计算部署，同时实现了更好的可扩展性和更高的资源利用率。与此同时，数据中心作为云计算的支柱，越来越多地包括异质计算和内存资源。向数据中心的无服务器计算和异类架构的转变产生了一个缺口，如果不加以解决，就会导致资源使用效率低下。该项目旨在解决这一差距，以便能够以更低的成本和更高的安全性在云中提供新的应用程序和新功能，为科学、工程和商业的发展提供平台。未来的数据中心将由不同的计算和内存组成。云中的应用程序的要求越来越多样化，例如并行度和粒度；内存延迟、容量和带宽要求；以及安全和隐私要求。该项目研究了无服务器计算作为一种很有前途的异质平台编程模型。无服务器平台将系统管理与应用程序执行分离：应用程序提供处理数据的功能，并让平台决定何时运行该功能、使用什么输入数据以及在什么物理机上运行。目前的平台，如AWS Lambda、Google Compute Functions或Azure Functions并未完全实现这一愿景，因为它们既不公开异类资源，也不自动管理所有资源。该项目探索了新的计算抽象，扩展了无服务器功能以更好地利用独特的硬件特征，并探索了内存以允许更自动地利用工作负载特征，如局部性和计算密集度。该奖项反映了NSF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。

Cloud computing has been a dominant computing paradigm that enables many important computing capabilities including large-scale (big) data processing, artificial intelligence, and scientific discoveries. A recent evolution of cloud computing includes the move to serverless computing, which simplifies the deployment of computation while enabling better scaling and higher resource utilization. Meanwhile, datacenters, the backbone of cloud computing, increasingly include heterogeneous compute and memory resources. The move toward serverless computing and heterogeneous architecture of datacenters produces a gap that unless addressed, results in inefficient use of resources. The project seeks to address this gap in order to enable new applications and new functionalities to be provided in the cloud, at lower cost and higher security, providing platforms for the advancement of science, engineering, and commerce. Future datacenters will consist of heterogeneous compute and memory. Applications in the cloud are increasingly varied in their requirements, such as degree and granularity of parallelism; memory latency, capacity, and bandwidth requirements; and security and privacy requirements. This project investigates serverless computing as a promising programming model for heterogeneous platforms. Serverless platforms decouple system management from application execution: applications provide functions that manipulate data, and leave it to the platform to determine when the function should run, with what input data, and on what physical machine. Current platforms, such as AWS Lambda, Google Compute Functions or Azure Functions do not fully implement this vision, as they do not expose heterogeneous resources nor manage all resources automatically. This project explores novel abstractions for compute that extend serverless functions to better leverage unique hardware characteristics, and for memory to allow more automated leveraging of workload characteristics such as locality and compute intensity.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.