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

机器学习(ML)正被用于开发智能软件的新方法。与此同时，无服务器计算正在重新定义如何使用云计算平台。使用无服务器计算，ML专家只需要定义一组可以访问公共数据存储的无状态函数。当前的ML软件系统通常专门用于第一代云计算系统，这些系统不具备无服务器基础设施所需的灵活性。该项目旨在利用无服务器计算来部署机器学习软件。这简化了部署，避免了基础设施维护，并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，无服务器计算已成为构建下一代ML服务和应用的趋势。该项目提出了统一的无服务器计算框架，旨在灵活、敏捷和高效地将ML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Serverful ML模型和无服务器云平台之间的语义鸿沟，本项目确定了三个主要任务：细粒度计算管理、高效的通信策略和高性价比的服务模型。该项目旨在通过删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。这是一个连续的过程，开发人员和运营团队越来越习惯于增加自动化和抽象，并且更容易将ML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。其结果是，开发人员可以自由地将正确的工具用于正确的任务，并轻松地构建跨越任何数量的不同无服务器服务的ML应用程序。这个项目强调开源软件开发。这将加强获得流动数据分析框架的机会，并扩大该项目的影响。此外，这个项目的模型和工作负荷/跟踪可能会使其他人能够进行进一步的研究。Repository[https://abclab-uncc.github.io/website/grants.html]项目(数据、代码、结果、模拟器和模拟器)将在未来5年内保留。这一奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.