CAREER: Automated and Efficient Machine Learning as a Service

职业：自动化高效的机器学习即服务

• 批准号: 2305491
• 负责人: Feng Yan
• 金额: $ 51.75万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2305491&HistoricalAwards=false
• 关键词: CAREER; Automated; Efficient; Machine; Learning

Machine-Learning-as-a-Service (MLaaS) is an emerging computing paradigm that provides optimized execution of machine learning tasks, such as model design, model training, and model serving, on cloud infrastructure. Explosive growth in model complexity and data size along with the surging demands of MLaaS is already resulting in substantial increases in computational resource and energy requirements. Unfortunately, existing MLaaS systems have poor resource management and limited support for user specified performance and cost requirements, exacerbating waste in computing resources and energy. This project aims to utilize the unique features of MLaaS to design efficient, automated, and user-centric MLaaS systems. This approach will significantly reduce resource waste and shorten the model design cycles through a variety of novel optimization approaches and by eliminating candidate models that fail to meet model serving latency and target accuracy. To support complete MLaaS workflow, this project will also develop MLaaS model serving methodologies that can meet service level latency requirements with minimum resource consumption using intelligent autoscaling.This project has the potential to tremendously reduce the resource and energy consumptions as well as the carbon footprint associated with the fast-growing societal demands in machine learning and cloud computing. Important insights and technologies will be produced targeting resource management and energy saving of the next-generation machine learning systems and cloud infrastructure. The findings of this project will also contribute to related fields of parallel and distributed systems, performance evaluation and optimization, and green computing. This project will carry out substantial integrated education activities including new course and online education development, integration of industry feedback in education. Additionally, the work will impact undergraduate and graduate students by training them in the art of system optimization combined with the latest machine learning domain knowledge while combining outreach and engagement of students from underrepresented groups and especially women.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.

机器学习即服务 (MLaaS) 是一种新兴的计算范例，可在云基础设施上提供机器学习任务的优化执行，例如模型设计、模型训练和模型服务。模型复杂性和数据规模的爆炸式增长以及 MLaaS 需求的激增已经导致计算资源和能源需求大幅增加。不幸的是，现有的MLaaS系统资源管理较差，对用户指定的性能和成本要求的支持有限，加剧了计算资源和能源的浪费。该项目旨在利用 MLaaS 的独特功能来设计高效、自动化且以用户为中心的 MLaaS 系统。这种方法将通过各种新颖的优化方法以及消除无法满足模型服务延迟和目标精度的候选模型，显着减少资源浪费并缩短模型设计周期。为了支持完整的 MLaaS 工作流程，该项目还将开发 MLaaS 模型服务方法，该方法可以使用智能自动扩展以最小的资源消耗满足服务级别延迟要求。该项目有可能大大减少资源和能源消耗以及相关的碳足迹随着机器学习和云计算快速增长的社会需求。将产生针对下一代机器学习系统和云基础设施的资源管理和节能的重要见解和技术。该项目的研究成果也将为并行与分布式系统、性能评估与优化、绿色计算等相关领域做出贡献。该项目将开展实质性的融合教育活动，包括新课程和在线教育开发、行业反馈融入教育等。 此外，这项工作还将对本科生和研究生产生影响，对他们进行系统优化艺术与最新机器学习领域知识的培训，同时结合来自代表性不足群体（尤其是女性）的学生的外展和参与。该奖项反映了 NSF 的法定使命，并已通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。

项目成果

Noctua: Towards Practical and Automated Fine-grained Consistency Analysis

Noctua：迈向实用且自动化的细粒度一致性分析

• 发表时间: 2024-03
• 期刊: European Conference on Computer Systems
• 作者: Ma, Kai;Li, Cheng;Zhu, Enzuo;Chen, Ruichuan;Yan, Feng;Chen, Kang
• 通讯作者: Chen, Kang

SciLance: Mitigate Load Imbalance for Parallel Scientific Applications in Cloud Environments

SciLance：缓解云环境中并行科学应用程序的负载不平衡

• DOI: 10.1109/cluster52292.2023.00012
• 发表时间: 2023-10
• 期刊: IEEE International Conference on Cluster Computing
• 作者: Wang, Xinying;Wan, Lipeng;Klasky, Scott;Zhao, Dongfang;Yan, Feng
• 通讯作者: Yan, Feng

AI augmented Edge and Fog computing: Trends and challenges

人工智能增强边缘和雾计算：趋势和挑战

• DOI: 10.1016/j.jnca.2023.103648
• 发表时间: 2023-07
• 期刊: Journal of Network and Computer Applications
• 影响因子: 8.7
• 作者: Tuli, Shreshth;Mirhakimi, Fatemeh;Pallewatta, Samodha;Zawad, Syed;Casale, Giuliano;Javadi, Bahman;Yan, Feng;Buyya, Rajkumar;Jennings, Nicholas R.
• 通讯作者: Jennings, Nicholas R.

MalleTrain: Deep Neural Networks Training on Unfillable Supercomputer Nodes

MalleTrain：在不可填充的超级计算机节点上进行深度神经网络训练

• DOI: 10.1145/3629526.3645035
• 发表时间: 2024-04-24
• 期刊: Proceedings of the 15th ACM/SPEC International Conference on Performance Engineering
• 作者: Xiaolong Ma;Feng Yan;Lei Yang;Ian T. Foster;M. Papka;Zhengchun Liu;R. Kettimuthu
• 通讯作者: R. Kettimuthu

A Generic, High-Performance, Compression-Aware Framework for Data Parallel DNN Training

用于数据并行 DNN 训练的通用、高性能、压缩感知框架

• DOI: 10.1109/tpds.2023.3266246
• 发表时间: 2024-01
• 期刊: IEEE Transactions on Parallel and Distributed Systems
• 影响因子: 5.3
• 作者: Wu, Hao;Wang, Shiyi;Bai, Youhui;Li, Cheng;Zhou, Quan;Yi, Jun;Yan, Feng;Chen, Ruichuan;Xu, Yinlong
• 通讯作者: Xu, Yinlong