CNS Core: Small: Collaborative Research: A Stochastic Resource Allocation and Task Assignment Framework for Mobile Edge Computing

移动边缘计算(MEC)是支持快速增长的低延迟移动应用和物联网(IoT)系统的新兴平台。它涉及在移动网络边缘部署具有计算、存储和通信功能的节点，并利用客户端移动设备在最终用户附近提供计算和数据处理服务。该项目解决了MEC系统中资源分配和任务分配的挑战，实现了边缘节点、远程云数据中心和移动用户之间的横向和纵向协作，以优化具有不同服务质量(Qos)要求的多种服务的性能。利用深度学习、随机博弈和优化技术，设计了MEC系统中协作资源分配和任务分配的随机框架。研究议程包括几个协同推进。首先，将研究MEC系统中不同实体之间的复杂相互作用，将制定新的优化问题，并将开发新的集中式和分布式算法。这些算法使服务提供商能够在随机和时变的网络状态和任务到达情况下最优地协调其计算任务。还将设计轻量级启发式算法，并与最优解进行比较。第二，制定网络分片和资源分配方案，让多个服务提供商动态共享虚拟的异构MEC网络基础设施，共同优化不同服务的资源利用和任务分配。第三，将通过概念验证原型验证在该项目下开发的算法，并使用一个实验试验台来验证。在该项目中开发的MEC解决方案预计将显著增强许多新兴的低延迟移动服务的用户体验，如智能交通和智慧城市。研究成果，再加上行业合作，将为技术转让提供新的想法，并将影响相关行业标准。拟议的研究完全纳入了培养本科生和研究生的教育计划。该项目还将通过与各种组织接触，扩大任职人数不足群体的参与。项目成果，包括技术报告、演示文稿、结果、数据集、软件和其他人工制品，将在网上提供。在本项目期间和完成后至少五年内将维护一个数据库。这一奖励反映了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。

Mobile edge computing (MEC) is an emerging platform for supporting rapidly growing low-latency mobile applications and Internet of Things (IoT) systems. It involves deploying nodes with computing, storage, and communication capabilities at the edge of mobile networks and utilizing client mobile devices for computing and data processing services near the end users. This project addresses the challenges of resource allocation and task assignment in MEC systems, enabling horizontal and vertical cooperation among edge nodes, remote cloud data centers, and mobile users for performance optimization of multiple services with different quality of service (QoS) requirements. Specifically, a stochastic framework for cooperative resource allocation and task assignment in MEC systems is designed using deep learning, stochastic game, and optimization techniques. The research agenda includes several synergistic thrusts. First, the complex interactions among the various entities of an MEC system will be investigated, new optimization problems will be formulated, and novel centralized and distributed algorithms will be developed. These algorithms enable a service provider to optimally orchestrate its computational tasks under stochastic and time-varying network states and task arrivals. Lightweight heuristic algorithms will also be designed and compared with the optimal solutions. Second, network slicing and resource allocation schemes will be developed so as to allow multiple service providers to dynamically share a virtualized heterogeneous MEC network infrastructure and jointly optimize resource utilization and task assignment for different services. Third, an experimental testbed will be instrumented and used to validate through proof-of-concept prototyping the algorithms developed under this project.The MEC solutions developed in this project are expected to significantly enhance user experience for many emerging low-latency mobile services such as intelligent transportation and smart cities. The research outcomes, coupled with industry collaboration, will provide fresh ideas for technology transfer and will impact related industry standards. The proposed research is fully integrated into the educational plan to train undergraduate and graduate students. The project will also broaden participation of underrepresented groups by reaching out to various organizations. Project outcomes, which include technical reports, presentations, results, datasets, software, and other artifacts, will be made available online. A data repository will be maintained during the course of this project and for at least five additional years after its completion.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.