CNS Core: Small: Moving Machine Learning into the Next-Generation Cloud Flexibly, Agilely and Efficiently

CNS核心：小：灵活、敏捷、高效地将机器学习迁移到下一代云

• 批准号: 2008265
• 负责人: Dong Dai
• 金额: $ 47.18万
• 依托单位: University of North Carolina at Charlotte
• 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: 2020-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008265&HistoricalAwards=false
• 关键词: CNS; Core; Small; Moving; Machine

Machine Learning (ML) is being used in new ways to develop intelligent software. Meanwhile, serverless computing is redefining how to use cloud computing platforms. With serverless computing, ML specialists only need to define a set of stateless functions that have access to a common data store. Current ML software systems are generally specialized for first-generation cloud computing systems that do not have the flexibility required for serverless infrastructures. This project aims to take advantage of serverless computing to deploy machine learning software. This simplifies the deployments, avoids the need for infrastructure maintenance, and includes built-in scalability and cost-control. Due to the ease of management and ability to rapidly scale, serverless computation has become the trend to build next-generation ML services and applications. This project proposes a unified serverless computing framework that aims to be flexible, agile and efficient for moving ML into the next-generation cloud to achieve better simplicity, manageability and productivity. In particular, to bridge the semantic gap between the serverful ML model and the serverless cloud platform, this project identifies three major tasks: fine-grained computation management, an efficient communication strategy and a cost-effective service model. This project aims for widespread serverless computing by removing server and operation system level details and simplifying the process of building and managing ML applications. It is a continuum along which developers and operations teams become more accustomed to increased automation and abstraction, and more comfortable breaking ML applications into simple, easy-to- manage microservices, application interfaces, and functions. The result is that developers are free to target the right tools for the right tasks and to build ML applications easily that span any number of different serverless services. This project emphasizes open-source software development. This will enhance the access to stream data analytic frameworks and broaden the project’s impact. Furthermore, the models and workloads/traces from this project may enable further research by others. The project repository[https://abclab-uncc.github.io/website/grants.html] (data, code, results, emulators, and simulators) will be maintained in the next 5 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.

与此同时，机器学习 (ML) 正在以新的方式用于开发智能软件，而无服务器计算正在重新定义如何使用云计算平台，ML 专家只需定义一组可以访问通用功能的无状态函数。当前的机器学习软件系统通常专门用于不具备无服务器基础设施所需的灵活性的第一代云计算系统，该项目旨在利用无服务器计算来部署机器学习软件。需要用于基础设施维护，并且包括内置的可扩展性和成本控制，由于易于管理和快速扩展的能力，无服务器计算已成为构建下一代机器学习服务和应用程序的趋势。该项目提出了统一的无服务器计算。该项目确定了一个框架，旨在灵活、敏捷和高效地将机器学习迁移到下一代云中，以实现更好的简单性、可管理性和生产力，特别是为了弥合服务器机器学习模型和无服务器云平台之间的语义差距。三大任务：该项目旨在通过消除服务器和操作系统级别的细节并简化构建和管理 ML 应用程序的过程来实现广泛的无服务器计算。开发人员和运营团队变得更加习惯于提高自动化和抽象性，并且更愿意将机器学习应用程序分解为简单、易于管理的微服务、应用程序接口和功能，结果是开发人员可以自由地为正确的目标选择正确的工具。任务和构建机器学习应用程序可以轻松跨越任意数量的不同无服务器服务，这将增强对流数据分析框架的访问并扩大该项目的影响力。其他人的进一步研究将在接下来的 5 年内进行维护。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

Drill: Log-based Anomaly Detection for Large-scale Storage Systems Using Source Code Analysis

演练：使用源代码分析对大规模存储系统进行基于日志的异常检测

• DOI: 10.1109/ipdps54959.2023.00028
• 发表时间: 2023-05
• 期刊: 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS
• 作者: Zhang, Di;Egersdoerfer, Chris;Mahmud, Tabassum;Zheng, Mai;Dai, Dong
• 通讯作者: Dai, Dong

A Shared Memory Cache Layer across Multiple Executors in Apache Spark

Apache Spark 中跨多个执行器的共享内存缓存层

• DOI: 10.1109/bigdata50022.2020.9378179
• 发表时间: 2020-12
• 期刊: 2020 IEEE International Conference on Big Data (Big Data
• 作者: Rang, Wei;Yang, Donglin;Cheng, Dazhao
• 通讯作者: Cheng, Dazhao