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

• 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

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.

无服务器计算平台是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管理用户的底层计算资源。无服务器计算有可能影响科学计算、大规模优化、深度神经网络（DNN）和实时视频编码等领域，因为它在计算能力方面易于管理、成本低廉、具有巨大的可扩展性和灵活性。然而，为了充分利用潜力，必须解决无服务器计算的几个独特属性的几个基本挑战，即短暂的机器，低内存占用，沉重的通信成本和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，无论是有意还是无意，都从云计算的有效性和普遍性中受益，从网络搜索到在线交易，云计算已经成为现代数字生活中的一切。由于无服务器平台预计将在不久的将来主导云计算，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。该提案采用了一种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。这将导致理论和算法的发展，这些理论和算法由编码和信息理论，资源分配和优化，随机线性代数以及机制设计和博弈论所告知的理论原则驱动。该项目由两个主要部分组成：（a）通过将编码计算的能力整合到优化框架中，实现无服务器系统中延迟最小化的鲁棒、高效和大规模可扩展的分布式计算算法;以及（B）用于成本最小化的最优定价方案，其利用激励机制进行定价，以最大化跨客户的总效用，该奖项反映了NSF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

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

• 发表时间: 2023
• 期刊: Proceedings of the International Workshop on Artificial Intelligence and Statistics
• 作者: Erginbas, Yigit Efe;Phade, Soham;Ramchandran, Kannan
• 通讯作者: Ramchandran, Kannan

OverSketched Newton: Fast Convex Optimization for Serverless Systems

• DOI: 10.1109/bigdata50022.2020.9378289
• 发表时间: 2019-03
• 期刊: 2020 IEEE International Conference on Big Data (Big Data)
• 作者: Vipul Gupta;S. Kadhe;T. Courtade;Michael W. Mahoney;K. Ramchandran

Serverless Straggler Mitigation using Error-Correcting Codes

• DOI: 10.1109/icdcs47774.2020.00019
• 发表时间: 2020-11
• 期刊: 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)
• 作者: Vipul Gupta;Dominic Carrano;Yaoqing Yang;Vaishaal Shankar;T. Courtade;K. Ramchandran

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

• 发表时间: 2020-10
• 作者: Amirali Aghazadeh;Vipul Gupta;Alex DeWeese;O. O. Koyluoglu-O.;K. Ramchandran