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

协作研究：PPoSS：规划：在异构数据中心上扩展安全无服务器计算

• 批准号: 2028818
• 负责人: Michael Swift
• 金额: $ 8.98万
• 依托单位: University of Wisconsin-Madison
• 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 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028818&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Scaling

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.

云计算已经成为一种占主导地位的计算范式，它实现了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向无服务器计算的迁移，这简化了计算的部署，同时实现了更好的可伸缩性和更高的资源利用率。同时，作为云计算支柱的数据中心越来越多地包含异构计算和内存资源。向无服务器计算和数据中心异构体系结构的转变产生了一个缺口，除非解决这个缺口，否则将导致资源的低效使用。该项目旨在解决这一差距，以便在云中以更低的成本和更高的安全性提供新的应用程序和新功能，为科学、工程和商业的进步提供平台。未来的数据中心将由异构计算和内存组成。云中的应用程序的需求越来越多样化，例如并行度和粒度；内存延迟、容量和带宽需求；安全和隐私要求。这个项目研究无服务器计算作为一个有前途的异构平台编程模型。无服务器平台将系统管理与应用程序执行分离：应用程序提供操作数据的功能，并将其留给平台来决定功能何时运行、使用什么输入数据以及在什么物理机器上运行。目前的平台，如AWS Lambda、谷歌Compute Functions或Azure Functions并没有完全实现这一愿景，因为它们没有公开异构资源，也没有自动管理所有资源。这个项目探索新的计算抽象，扩展无服务器功能以更好地利用独特的硬件特性，并为内存探索新的抽象，以允许更自动地利用工作负载特性，如局部性和计算强度。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。

项目成果

Tile-based Lightweight Integer Compression in GPU

• DOI: 10.1145/3514221.3526132
• 发表时间: 2022-06
• 期刊: Proceedings of the 2022 International Conference on Management of Data
• 作者: Anil Shanbhag;B. Yogatama;Xiangyao Yu;S. Madden

FlexPushdownDB: Hybrid Pushdown and Caching in a Cloud DBMS

• DOI: 10.14778/3476249.3476265
• 发表时间: 2021-07
• 期刊: Proc. VLDB Endow.
• 作者: Yifei Yang;Matt Youill;M. Woicik;Yizhou Liu;Xiangyao Yu;M. Serafini;Ashraf Aboulnaga;M. Stonebraker

Orchestrating data placement and query execution in heterogeneous CPU-GPU DBMS

在异构 CPU-GPU DBMS 中编排数据放置和查询执行

• DOI: 10.14778/3551793.3551809
• 发表时间: 2022
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Yogatama, Bobbi W.;Gong, Weiwei;Yu, Xiangyao
• 通讯作者: Yu, Xiangyao