CAREER: Achieving Ultra-Low Latency under Heterogeneity and Uncertainty in Edge Computing

边缘计算被设想为一种超越云计算的范式，支持自动驾驶、增强现实和自动化移动机器人等新兴应用。然而，为了实现边缘计算所设想的延迟突破并将这一新范式付诸实施，仍然缺少的一个关键部分是协调数据和计算以保证超低延迟的算法。这项职业计划的总体目标是通过开发(I)动态协调边缘计算系统的数据和计算以满足严格的延迟目标的编排算法来填补这一空白，以及(Ii)表征边缘计算系统的基本资源需求和最佳操作点的理论基础。该方案中实现超低延迟的算法创新和配置洞察将指导边缘计算系统的大规模部署，极大地有利于延迟敏感型边缘应用，如老年人和残疾人认知辅助和自动驾驶等具有较强社会影响的应用。这一提议下的理论进步将为随机系统的研究做出基础性贡献，为电气工程、计算机科学和运筹学的交叉学科研究社区创造新的研究重点。这项建议将对教育和社区产生重大影响。理论方法和实验平台都将被纳入卡内基梅隆大学研究生和本科生的课程和课程项目。还将利用在线平台传播与该项目有关的教育和研究材料，以扩大影响。该项目的目标是开发(I)动态协调边缘计算系统的数据和计算以满足严格的延迟目标的协调算法；(Ii)描述边缘计算系统的基本资源需求和最佳运行点的理论基础。具体地说，这一目标将分别在EDGE系统的两种典型操作模式(推力I和推力II)中实现，基于这两种模式，边缘节点被授权处理由客户端产生的数据，并且其计算能力被利用。然后，通信和计算环境中的不确定性将在基于学习的正交推力(推力III)协调中得到解决。拟议的研究将导致目前缺少的算法创新和提供所需的洞察力，以保证边缘计算系统中的超低延迟。具体地说，将开发协调算法，以联合和动态地利用新兴的5G及以上无线技术下的通信资源，以及边缘服务器和边缘客户端的分散计算能力。该建议中的交叉方法是基于这样的观察，即未来的边缘系统将是大规模的，并且该方法建立在大规模随机系统的重要最近结果的基础上。这些结果表明，在正确的编排算法下，可以在大型系统中同时实现超低延迟和高系统利用率。这一建议将进一步推进大规模随机系统的理论，使其具有更大的普遍性，以解决异构性、不确定性、不同类型资源之间的相互作用以及基于动态性能的作业执行。这些都是在EDGE系统和现代应用中出现的新的独特挑战，在传统方法中被高度忽视。该奖项反映了NSF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。

Edge computing has been envisioned to be a paradigm beyond cloud computing that supports emerging applications such as autonomous driving, augmented reality, and automated mobile robots. However, to realize the envisioned latency breakthrough of edge computing and put this new paradigm into operation, a critical piece that is still missing is algorithms that orchestrate the data and the computation to guarantee ultra-low latency. The overall objective of this CAREER proposal is to fill this gap by developing (i) orchestration algorithms that dynamically coordinate data and computation for edge computing systems to meet stringent latency goals, and (ii) theoretical foundations to characterize the fundamental resource requirements and optimal operating points of edge computing systems. The algorithmic innovation and provisioning insights for achieving ultra-low latency in this proposal will guide the deployment of edge computing systems in large scale, greatly benefiting latency sensitive edge applications with strong societal impacts such as cognitive assistance for the elderly and disabled and autonomous driving. The theoretical advances under this proposal will make fundamental contributions to research in stochastic systems, creating new research focuses for interdisciplinary research communities at the intersection of electrical engineering, computer science, and operations research. This proposal will have significant educational and community impact. Both the theoretical approaches and the experiment platforms will be incorporated into the curriculum and course projects at graduate and undergraduate levels at Carnegie Mellon University. Online platforms will also be leveraged to disseminate educational and research materials related to this project for a greater reach. Continuing and expanded efforts will be spent on STEM outreach activities to K-12 students, mentoring students from underrepresented groups for research, promoting the visibility of researchers from underrepresented groups, and initiating online seminars to outreach to the general public.The goal of this project is to develop (i) orchestration algorithms that dynamically coordinate data and computation for edge computing systems to meet stringent latency goals, and (ii) theoretical foundations to characterize the fundamental resource requirements and optimal operating points of edge computing systems. In particular, this goal will be achieved in two representative operating modes of edge systems (Thrusts I and II), respectively, based on which edge nodes are authorized to process the data generated by clients and whose computing power is being exploited. Then the uncertainty in communication and computation environments will be addressed in an orthogonal thrust (Thrust III) learning-based orchestration. The proposed research will result in the currently missing algorithmic innovation and provisioning insights needed for guaranteeing ultra-low latency in edge computing systems. Specifically, orchestration algorithms will be developed to jointly and dynamically utilize the communication resources under the emerging 5G and beyond wireless technologies and the dispersed computing power of edge servers and edge clients. The cross-cutting approach in this proposal is motivated by the observation that future edge systems will be of large scale, and the approach builds upon significant recent results on large-scale stochastic systems. These results demonstrate that with the right orchestration algorithms, it is possible to achieve ultra-low latency and high system utilization simultaneously in large systems. This proposal will further advance the theory for large-scale stochastic systems to a much greater generality to address heterogeneity, uncertainty, interactions among different types of resources, and dynamic performance-based job execution. These are new unique challenges arising in edge systems and modern applications in general that are highly underexplored in traditional approaches.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.