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

CNS 核心：小型：协作研究：移动边缘计算的随机资源分配和任务分配框架

基本信息

批准号：
1909562
负责人：
Marwan Krunz
金额：
$ 25万
依托单位：
University of Arizona
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909562&HistoricalAwards=false
关键词：
CNS Core Small Collaborative Research

项目摘要

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.

移动的边缘计算（MEC）是用于支持快速增长的低延迟移动的应用和物联网（IoT）系统的新兴平台。它涉及在移动的网络边缘部署具有计算、存储和通信能力的节点，并利用客户端移动的设备在终端用户附近提供计算和数据处理服务。该项目解决MEC系统中资源分配和任务分配的挑战，实现边缘节点、远程云数据中心和移动的用户之间的横向和纵向协作，以实现具有不同服务质量（QoS）要求的多种服务的性能优化。具体而言，MEC系统中的合作资源分配和任务分配的随机框架设计使用深度学习，随机博弈和优化技术。研究议程包括几个协同推进。首先，MEC系统的各种实体之间的复杂的相互作用将被调查，新的优化问题将制定，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根据随机和时变的网络状态和任务到达优化编排其计算任务。轻量级的启发式算法也将被设计和最佳解决方案进行比较。其次，将开发网络切片和资源分配方案，以便允许多个服务提供商动态共享虚拟化异构MEC网络基础设施，并联合优化不同服务的资源利用和任务分配。第三，将建立一个实验测试平台，通过概念验证原型来验证本项目开发的算法。本项目开发的MEC解决方案预计将显著增强许多新兴低延迟移动的服务（如智能交通和智慧城市）的用户体验。研究成果加上行业合作，将为技术转让提供新的思路，并将影响相关的行业标准。拟议的研究完全纳入教育计划，以培养本科生和研究生。该项目还将通过与各组织联系，扩大代表性不足群体的参与。项目成果，包括技术报告、演示文稿、结果、数据集、软件和其他工件，将在网上提供。该奖项反映了NSF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。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Latency Prediction for Delay-sensitive V2X Applications in Mobile Cloud/Edge Computing Systems

DOI：
10.1109/globecom42002.2020.9348104
发表时间：
2020-12
期刊：
GLOBECOM 2020 - 2020 IEEE Global Communications Conference
影响因子：
0
作者：
Wenhan Zhang;Mingjie Feng;M. Krunz;H. Volos
通讯作者：
Wenhan Zhang;Mingjie Feng;M. Krunz;H. Volos

A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network

DOI：
10.1109/icc40277.2020.9149319
发表时间：
2020-03
期刊：
ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
影响因子：
0
作者：
Rong Xia;Yong Xiao;Yingyu Li;M. Krunz;D. Niyato
通讯作者：
Rong Xia;Yong Xiao;Yingyu Li;M. Krunz;D. Niyato

Program Placement Optimization for Storage-constrained Mobile Edge Computing Systems: A Multi-armed Bandit Approach

DOI：
10.1109/wowmom51794.2021.00028
发表时间：
2021-06
期刊：
2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)
影响因子：
0
作者：
Ming Feng;M. Krunz
通讯作者：
Ming Feng;M. Krunz

AdaptiveFog: A Modelling and Optimization Framework for Fog Computing in Intelligent Transportation Systems