CNS Core: Small: DeepEdge: QoE-based Resource Allocation for Future Heterogeneous and Dynamic Edge-IoT Applications

CNS 核心：小型：DeepEdge：面向未来异构和动态边缘物联网应用的基于 QoE 的资源分配

• 批准号: 1909520
• 负责人: Jianli Pan
• 金额: $ 50万
• 依托单位: University of Missouri-Saint Louis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909520&HistoricalAwards=false
• 关键词: CNS; Core; Small; DeepEdge; QoE

The emerging Internet of Things (IoT) is connecting increasing numbers of smart devices and enabling varieties of heterogeneous IoT applications, empowered by cloud and edge computing technologies. In particular, edge or fog computing technologies will significantly benefit IoT applications that are delay-sensitive, bandwidth/data intensive, or that require closer intelligence. However, for effective Edge-IoT resource allocation, significant challenges exist due to the following requirements and constraints: 1) the demand side that a massive number of IoT devices can run heterogeneous applications with various Quality of Service (QoS) requirements and different priorities; and 2) the supply side that the edge clouds need to dynamically and optimally allocate limited and multidimensional resources (CPU, storage, and bandwidth) at geospatially distributed points. The objective of this project is to respond to these challenges by designing and developing a new Edge-IoT framework named DeepEdge using deep online learning that allocates resources to heterogeneous IoT applications and dynamic IoT devices to maximize users' Quality of Experience (QoE). The proposed research will advance knowledge and fundamentally change the way future edge computing systems work in supporting heterogeneous and dynamic IoT applications. The transformative research outcomes will benefit users and society with inexpensive and effective IoT application delivery, and contribute to important societal challenges in supporting emerging IoT devices and applications. The project will also broadly involve and impact K-12 underrepresented groups and female students in computer science, and develop strong research and education integration for various levels of students. The goal of the proposed research is to develop the framework, model, and algorithms in effectively delivering heterogeneous IoT applications on edge clouds and provisioning high-quality QoE for users. More specifically, the project will result in: i) a new QoE model to quantify the users satisfaction and its related factors including multiple applications QoS requirements and applications priority; ii) a new deep machine learning based two-stage resource allocation scheme that will adapt application QoS requirements according to available resources at the edge cloud and maintain application's priority, and will intelligently and jointly allocate communication and computation resources with the objective of maximization of users QoE; iii) a novel deep Q-learning scheme that will dynamically select the most appropriate edge nodes to handle the multiple application tasks with the goal to optimize the task execution delay; iv) a hardware and software test-bed implementation to validate and evaluate the effectiveness, efficiency, and the practicality of the proposed research.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.

新兴的物联网 (IoT) 在云和边缘计算技术的支持下，正在连接越来越多的智能设备，并支持各种异构物联网应用。特别是，边缘或雾计算技术将显着有利于延迟敏感、带宽/数据密集型或需要更紧密智能的物联网应用。然而，对于有效的边缘物联网资源分配，由于以下要求和限制而存在重大挑战：1）大量物联网设备可以运行具有各种服务质量（QoS）要求和不同优先级的异构应用程序的需求方； 2）边缘云需要在地理空间分布点动态、优化分配有限的多维资源（CPU、存储和带宽）的供应方。该项目的目标是通过使用深度在线学习设计和开发名为 DeepEdge 的新边缘物联网框架来应对这些挑战，该框架将资源分配给异构物联网应用程序和动态物联网设备，以最大限度地提高用户的体验质量 (QoE)。拟议的研究将增进知识并从根本上改变未来边缘计算系统在支持异构和动态物联网应用方面的工作方式。变革性的研究成果将通过廉价且有效的物联网应用交付使用户和社会受益，并有助于应对支持新兴物联网设备和应用的重要社会挑战。该项目还将广泛涉及和影响 K-12 计算机科学领域代表性不足的群体和女学生，并为各个级别的学生发展强有力的研究和教育一体化。本研究的目标是开发框架、模型和算法，以便在边缘云上有效交付异构物联网应用，并为用户提供高质量的 QoE。更具体地说，该项目将产生： i) 一个新的 QoE 模型，用于量化用户满意度及其相关因素，包括多个应用程序 QoS 要求和应用程序优先级； ii）一种新的基于深度机器学习的两阶段资源分配方案，该方案将根据边缘云的可用资源调整应用程序的QoS要求并保持应用程序的优先级，并以用户最大化为目标智能地联合分配通信和计算资源体验质量； iii) 一种新颖的深度 Q 学习方案，该方案将动态选择最合适的边缘节点来处理多个应用任务，以优化任务执行延迟； iv) 硬件和软件测试台实施，以验证和评估拟议研究的有效性、效率和实用性。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进行评估，被认为值得支持审查标准。

项目成果

I-HARF: Intelligent and Hierarchical Framework for Adaptive Resource Facilitation in Edge-IoT Systems

• DOI: 10.1109/jiot.2022.3151667
• 发表时间: 2023-03
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Ismail Alqerm;Jianli Pan

DeepEdge: A New QoE-Based Resource Allocation Framework Using Deep Reinforcement Learning for Future Heterogeneous Edge-IoT Applications

• DOI: 10.1109/tnsm.2021.3123959
• 发表时间: 2021-12-01
• 期刊: IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
• 影响因子: 5.3
• 作者: AlQerm, Ismail;Pan, Jianli
• 通讯作者: Pan, Jianli

Enhanced Online Q-Learning Scheme for Resource Allocation with Maximum Utility and Fairness in Edge-IoT Networks

• DOI: 10.1109/tnse.2020.3015689
• 发表时间: 2020-10-01
• 期刊: IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
• 影响因子: 6.6
• 作者: AlQerm, Ismail;Pan, Jianli
• 通讯作者: Pan, Jianli

ORCA: Enabling an Owner-Centric and Data-Driven Management Paradigm for Future Heterogeneous Edge-IoT Systems

ORCA：为未来异构边缘物联网系统实现以所有者为中心、数据驱动的管理范式

• DOI: 10.1109/mcom.001.2000237
• 发表时间: 2021
• 期刊: IEEE Communications Magazine
• 影响因子: 11.2
• 作者: Pan, Jianli;Wang, Jianyu;AlQerm, Ismail;Liu, Yuanni;Yang, Zhicheng
• 通讯作者: Yang, Zhicheng