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.

云计算一直是一个主要的计算范式，它可以使许多重要的计算功能在内，包括大规模（大）数据处理，人工智能和科学发现。云计算的最新演变包括转向无服务器计算，这简化了计算的部署，同时可以更好地缩放缩放和更高的资源利用。同时，数据中心是云计算的骨干，越来越多地包括异质计算和内存资源。朝着无服务器计算和数据中心的异质体系结构的转变会产生一个差距，除非解决，否则会导致资源效率低下。该项目旨在解决此差距，以便以较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，从而为发展科学，工程和商业提供平台。未来的数据中心将包括异质计算和内存。云中的应用在其需求中越来越多，例如平行性的程度和粒度；内存延迟，容量和带宽要求； 以及安全和隐私要求。该项目将无服务器计算作为异构平台的有前途的编程模型进行调查。无服务器平台从应用程序执行中解除系统管理：应用程序提供了操纵数据的功能，并将其留在平台上，以确定何时应运行该功能，使用什么输入数据以及什么物理机器。 当前平台，例如AWS Lambda，Google计算功能或Azure功能并不能完全实现此愿景，因为它们不会暴露异质资源或自动管理所有资源。该项目探讨了用于扩展无服务器功能以更好地利用独特硬件特性的新颖的计算抽象，并为记忆提供了更多的自动化杠杆作用，例如局部和计算强度等工作负载特征。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子的功能和广泛的影响来评估NSF的法定任务，并被认为是值得的。

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

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

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