CIF: Small: Foundations of Serverless Computing: Optimizing Latency and Utility

CIF：小型：无服务器计算的基础：优化延迟和实用性

基本信息

批准号：
2007669
负责人：
Kannan Ramchandran
金额：
$ 50万
依托单位：
University of California-Berkeley
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 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007669&HistoricalAwards=false
关键词：
CIF Small Foundations Serverless Computing

项目摘要

Serverless computing platforms represent the fastest growing segment of cloud services, and are predicted to dominate cloud computing in the near future. These platforms run user-specified functions and automatically manage the underlying compute resources for the users. Serverless computing has the potential to impact fields as diverse as scientific computing, large-scale optimization, deep neural networks (DNNs), and video encoding in real-time due to its ease of management, inexpensiveness, massive scalability and flexibility in terms of compute power. In order to harness the full potential, however, several fundamental challenges must be addressed concerning several unique attributes of serverless computing, namely ephemeral machines, low memory footprint, heavy communication costs, and substantially different pricing policies. This project approaches these unique challenges by combining cutting-edge techniques from distributed computing with innovative concepts from coding theory, information theory, optimization, and game theory. The broader societal impact of this project is best appreciated by considering that nearly everyone, knowingly or unknowingly, benefits from the efficacy and ubiquity of cloud computing which has come to underly everything in modern digital life from web searches to online transactions. As serverless platforms are expected to dominate cloud computing in the near future, this project is expected to provide significant benefits by increasing efficiencies and reducing the user costs of cloud services. This proposal takes a principled and foundational approach to minimizing latency and costs in serverless computing. This will lead to the development of theory and algorithms driven by theoretical principles which are informed by coding and information theory, resource allocation and optimization, randomized linear algebra, as well as mechanism design and game theory. The project consists of two major components: (a) Robust, efficient, and massively scalable distributed computing algorithms for latency minimization in serverless systems by integrating the power of coded computation into an optimization framework; and (b) Optimal pricing schemes for cost minimization, which leverage incentive mechanisms for pricing that maximize the total utility across customers, and enable cloud service providers to achieve favorable trade-offs between quality-of-service and revenue.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.

无服务器计算平台代表了云服务中增长最快的部分，预计在不久的将来将主导云计算。这些平台运行用户指定的功能并自动为用户管理底层计算资源。无服务器计算由于其易于管理、成本低廉、计算方面具有巨大的可扩展性和灵活性，有可能影响科学计算、大规模优化、深度神经网络 (DNN) 和实时视频编码等多个领域力量。然而，为了充分发挥潜力，必须解决与无服务器计算的几个独特属性有关的几个基本挑战，即短暂的机器、低内存占用、高昂的通信成本以及截然不同的定价政策。该项目通过将分布式计算的尖端技术与编码理论、信息论、优化和博弈论的创新概念相结合来应对这些独特的挑战。考虑到几乎每个人都有意或无意地受益于云计算的功效和普遍性，云计算已经成为现代数字生活中从网络搜索到在线交易的一切的基础，因此可以最好地理解该项目更广泛的社会影响。由于无服务器平台预计将在不久的将来主导云计算，因此该项目预计将通过提高效率和降低云服务的用户成本来提供显着的好处。该提案采用原则性和基础性的方法来最大限度地减少无服务器计算中的延迟和成本。这将导致由编码和信息论、资源分配和优化、随机线性代数以及机制设计和博弈论等理论原理驱动的理论和算法的发展。该项目由两个主要部分组成：(a) 稳健、高效且可大规模扩展的分布式计算算法，通过将编码计算的功能集成到优化框架中，实现无服务器系统中的延迟最小化； (b) 成本最小化的最佳定价方案，该方案利用定价激励机制，最大限度地提高客户的总效用，并使云服务提供商能够在服务质量和收入之间实现有利的权衡。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory

BEAR：草图次线性存储器中超高维特征选择的 BFGS 算法

DOI：
发表时间：
2021-01
期刊：
2021 2nd Annual Conference on Mathematical and Scientific Machine Learning
影响因子：
0
作者：
Amirali, Aghazadeh;Gupta, Vipul;DeWeese, Ale;Koyluoglu, O.Ozan;Ramchandran, Kannan
通讯作者：
Ramchandran, Kannan

Interactive Learning with Pricing for Optimal and Stable Allocations in Markets

交互式学习和定价，以实现市场的最佳和稳定配置

DOI：
发表时间：
2023-04
期刊：
Proceedings of the International Workshop on Artificial Intelligence and Statistics
影响因子：
0
作者：
Erginbas, Yigit Efe;Phade, Soham;Ramchandran, Kannan
通讯作者：
Ramchandran, Kannan

OverSketched Newton: Fast Convex Optimization for Serverless Systems

OverSketched Newton：无服务器系统的快速凸优化

DOI：
10.1109/bigdata50022.2020.9378289
发表时间：
2020-12
期刊：
2020 IEEE International Conference on Big Data (Big Data
影响因子：
0
作者：
Gupta, Vipul;Kadhe, Swanand;Courtade, Thomas;Mahoney, Michael W.;Ramchandran, Kannan
通讯作者：
Ramchandran, Kannan

Serverless Straggler Mitigation using Error-Correcting Codes

使用纠错代码缓解无服务器落后者

DOI：
10.1109/icdcs47774.2020.00019
发表时间：
2020-11
期刊：
2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS
影响因子：
0
作者：
Gupta, Vipul;Carrano, Dominic;Yang, Yaoqing;Shankar, Vaishaal;Courtade, Thomas;Ramchandran, Kannan
通讯作者：
Ramchandran, Kannan