CNS Core:Medium:Systems Challenges in Scaling Distributed Intelligent Applications

智能边缘设备的快速扩散和机器学习的突破将改变应用程序从传感器数据中提取见解的方式，创造出一类新的分布式智能应用程序（DIAs），包括视频监控、自主设备控制和网络安全响应。该项目探索了新的分析、分布和资源管理方法，使dia能够采用地理分布架构，在传感器、边缘集群和云数据中心之间传播分析。这种方法与目前在云中配置数据和分析的方法相比是一个重大转变。分布式智能应用（DIAs）面临着分布、突发和高计算需求以及复杂分析带来的根本性挑战。首先，该项目将探索跨传感器、边缘和云资源分发DIA分析的系统方法，以降低成本并提高应用质量。其次，该项目融合了分析和沟通，为优化能力和成本提供了新的选择。第三，为了满足实时和突发的资源需求，该项目将探索新的资源管理方法，为共享、开放的资源环境提供必要的应用保障。拟议的研究将创造能力，增加dia的数量和能力，使此类应用以更低的成本获得更高的准确性、及时性和效率。该项目将涉及许多本科生和研究生，让他们接触到真正的边缘计算测试平台、实验研究和令人兴奋的应用。这些机会和影响将包括课程丰富和本科生研究机会。项目团队将为几个大型拓展项目带来新的见解和能量，以扩大计算机的参与。该项目将发布项目生成的数据和源代码。在项目期间，这些软件构件将通过github （https://github.com/）或部门存储服务开源。在项目结束后，团队将提供最大的努力支持，帮助其他人使用项目结果，因为专业知识和团队工作是可用的。当机会出现时，我们将与行业伙伴合作，使结果和见解更广泛地得到应用。该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。

The rapid proliferation of smart edge devices and breakthroughs in machine learning are poised to change how applications extract insights from sensor data, creating a new class of Distributed Intelligent Applications (DIAs) that include video monitoring, autonomous device control, and response for network cybersecurity. The project explores new analysis, distribution, and resource management approaches that enable DIAs to employ geographically distributed architectures, spreading analytics across sensors, edge clusters, and cloud datacenters. This approach is a major shift from today's approach that collocates data and analytics in the cloud.Distributed Intelligent Applications (DIAs) present fundamental challenges arising from distribution, bursts and high computational demands, as well as complex analytics. First, the project will explore systematic approaches that distribute DIA analysis across sensor, edge, and cloud resources to reduce cost and increase application quality. Second, the project blends analytics and communication, enabling new options to optimize capability and cost. Third, to meet realtime and bursty resource requirements, the project will explore new resource management approaches for shared, open resource environments that provide essential application guarantees.The proposed research will create capabilities that will increase the number and the capability of DIAs, allowing such applications greater accuracy, timeliness, and efficiency at lower cost. The project will involve a number of undergraduate and graduate students, exposing them to real edge computing testbeds, experimental studies, and exciting applications. These opportunities and impacts will include course enrichment and undergraduate research opportunities. The project team will bring novel insights and energy to several large outreach programs to broaden participation in computing.The project will release data and source code generated by the project. Such software artifacts will be open sourced via github (https://github.com/) or departmental storage service, for the project period. After the project's end, the team will provide best-effort support, helping others to use the project results as expertise and team effort is available. As opportunity arises, we will work with industry partners to make results and insights even more broadly available.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.