Collaborative Research: CNS Core: Small: Optimizing Large-Scale Heterogeneous ML Platforms

合作研究：CNS Core：小型：优化大规模异构机器学习平台

• 批准号: 2146909
• 负责人: Zhenhua Liu
• 金额: $ 25万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146909&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

Large-scale artificial intelligence and machine learning (AI/ML) platforms are playing a vital role in the current data revolution. To minimize efforts from users, an end-to-end solution is desired to deploy complex workflow over possibly heterogeneous computing clusters. However, the scheduling and resource management problems behind such “push-button” deployment are challenging. If left unsolved, these costly systems will be severely under-utilized, leading to unnecessary electricity consumption and greenhouse gas emissions. This project will develop efficient resource allocation policies for distributed, large-scale AI/ML systems to tackle the challenges. Specifically, this project will accelerate and parallelize the large-scale optimization and inference tasks that dominate workloads in AI/ML platforms via distributed optimization that provides fault tolerance and robustness to stragglers in heterogeneous settings. Built upon the distributed optimization, the project will further schedule AI/ML workflows with precedence constraints among sub-tasks. Finally, heterogeneous resources are allocated among jobs fairly and efficiently in the case where the resources being allocated are exchangeable, which is key for AI/ML platforms with graphic processing units (GPUs) and other accelerators. The project will provide new fundamental algorithms for scheduling and resource allocation in AI/ML platforms used across academia and industry. The algorithmic ideas will be developed in the context of core, classical models and so will apply more broadly than AI/ML platforms, e.g., to networking, storage, supply chain management, and beyond. The project will seek to broaden the participation of underrepresented groups in Science, Technology, Engineering and Mathematics by planned activities including the development of accelerated mathematics programs for middle school students, summer programs for middle-school and high-school students, and summer research programs for undergraduate students.The project will make its software artifacts, datasets, and research results available to the research community on the project website at https://adamwierman.com/optimizing-large-scale-heterogeneous-ml-platforms/ Artifacts will be maintained for a minimum of 10 years.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.

大规模人工智能和机器学习 (AI/ML) 平台在当前的数据革命中发挥着至关重要的作用，为了最大限度地减少用户的工作量，需要一种端到端的解决方案来在可能的异构计算集群上部署复杂的工作流程。然而，这种“按钮式”部署背后的调度和资源管理问题具有挑战性，如果不解决，这些成本高昂的系统将严重得不到充分利用，导致不必要的电力消耗和温室气体排放。该项目将开发有效的资源分配。分布式、大规模的政策具体来说，该项目将通过分布式优化来加速和并行化主导 AI/ML 平台工作负载的大规模优化和推理任务，为异构环境中的落后者提供容错性和鲁棒性。通过分布式优化，该项目将进一步调度人工智能/机器学习工作流程，并在子任务之间设置优先级约束。最后，在分配的资源可互换的情况下，异构资源在作业之间公平、高效地分配，这是人工智能/机器学习的关键。平台该项目将为学术界和工业界使用的人工智能/机器学习平台中的调度和资源分配提供新的基础算法。算法思想将在核心、经典模型等的背景下开发。将比人工智能/机器学习平台应用更广泛，例如网络、存储、供应链管理等。该项目将寻求通过计划活动，包括开发加速的中学生的数学课程、中学生和高中生的暑期课程以及本科生的暑期研究计划。该项目将在项目网站上向研究界提供其软件工件、数据集和研究结果： https://adamwierman.com/optimizing-large-scale-heterogeneous-ml-platforms/ 工件将保留至少 10 年。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，认为值得支持智力价值和更广泛的影响审查标准。

项目成果

Deep Learning-Assisted Online Task Offloading for Latency Minimization in Heterogeneous Mobile Edge

• DOI: 10.1109/tmc.2023.3285882
• 发表时间: 2024-05
• 期刊: IEEE Transactions on Mobile Computing
• 影响因子: 7.9
• 作者: Yu Liu;Yingling Mao;Z. Liu;Yuanyuan Yang

Applied Online Algorithms with Heterogeneous Predictors

• 发表时间: 2023
• 作者: Jessica Maghakian;Russell Lee;M. Hajiesmaili;Jian Li;R. Sitaraman;Zhenhu Liu

Online Container Scheduling for Data-intensive Applications in Serverless Edge Computing

• DOI: 10.1109/infocom53939.2023.10229034
• 发表时间: 2023-05
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Xiaojun Shang;Yingling Mao;Yu Liu;Yaodong Huang;Zhen Liu;Yuanyuan Yang

Joint Task Offloading and Resource Allocation in Heterogeneous Edge Environments

• DOI: 10.1109/infocom53939.2023.10229015
• 发表时间: 2023-05
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Yu Liu;Yingling Mao;Z. Liu;Fan Ye;Yuanyuan Yang

Energy-Aware Online Task Offloading and Resource Allocation for Mobile Edge Computing

• DOI: 10.1109/icdcs57875.2023.00073
• 发表时间: 2023-07
• 期刊: 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS)
• 作者: Yu Liu;Yingling Mao;Xiaojun Shang;Z. Liu;Yuanyuan Yang