Collaborative Research: CNS Core: Small: Robust Resource Planning and Orchestration to Satisfy End-to-End SLA Requirements in Mobile Edge Networks

协作研究：CNS 核心：小型：强大的资源规划和编排，以满足移动边缘网络中的端到端 SLA 要求

• 批准号: 2007391
• 负责人: Ruozhou Yu
• 金额: $ 14.25万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007391&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

Mobile edge computing has emerged to address the long end-to-end latency, low throughput, and unpredictability of cloud computing as an Internet-based service when supporting modern mobile applications. Nevertheless, the lack of performance guarantees in the form of service-level agreements (SLAs) can lead to performance degradation of critical applications, rendering them incompetent or unsafe to use. Due to the high dynamics in the mobile environment, the edge provider can incur substantial financial risks for providing SLA guarantees that could be violated. This discourages edge providers from providing SLA guarantees without first understanding and being able to control the associated risks. This project seeks to develop tools that help the edge provider to quantify and minimize the risks associated with providing edge SLA guarantees on key performance metrics through resource planning and orchestration. This project will significantly advance our knowledge on the risk factors in edge computing and give rise to new frontiers in edge computing research. Also, this project will enable and enhance life-changing edge applications such as mobile vision and autonomous driving, promote investment and expedite development in the edge computing industry, train highly qualified personnel for the future computing workforce, and broaden awareness and interest in edge computing through curriculum development and research dissemination.This project will lay the theoretical and algorithmic foundation of comprehensive risk modeling and optimization for providing edge SLA guarantees. This project combines a realistic performance model of mobile edge applications with the established theory of risk management in portfolio management and develops efficient algorithms for risk assessment and optimization through stochastic optimization, convex optimization, sampling techniques, approximations, and learning-based methods. Specifically, this project makes the following technical contributions: 1) development and validation of a realistic performance and SLA model for mobile edge computing applications, 2) modeling and optimization of three risk measures (risk probability, value-at-risk, and conditional value-at-risk) for single-user risk-aware edge resource orchestration, 3) modeling and optimization for multi-user single-node risk-aware edge resource orchestration, and 4) modeling and optimization for multi-user multi-node risk-aware edge resource provisioning. All research outcomes will be evaluated with testbed and/or large-scale simulations with public traces, and will be made publicly available on the PIs' websites to promote result reproduction and future research advancements along the line.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.

移动边缘计算已经出现，以解决云计算作为基于Internet的服务的长端到端延迟，低吞吐量和不可预测性的性能。然而，缺乏服务级协议形式的性能保证（SLA）可能会导致关键应用程序的性能下降，从而使它们无能或不安全使用。由于移动环境中的高动力，Edge提供商可能会遇到实质性的财务风险，以提供可能违反的SLA保证。这阻止边缘提供商提供SLA保证，而无需先了解并能够控制相关风险。该项目旨在开发工具，以帮助边缘提供商量化和最大程度地减少与Edge SLA通过资源计划和编排为关键绩效指标提供的风险。该项目将大大提高我们对边缘计算中风险因素的了解，并引起边缘计算研究中的新边界。 Also, this project will enable and enhance life-changing edge applications such as mobile vision and autonomous driving, promote investment and expedite development in the edge computing industry, train highly qualified personnel for the future computing workforce, and broaden awareness and interest in edge computing through curriculum development and research dissemination.This project will lay the theoretical and algorithmic foundation of comprehensive risk modeling and optimization for providing edge SLA guarantees.该项目将移动边缘应用程序的现实性能模型与投资组合管理中的风险管理理论结合在一起，并通过随机优化，凸优化，采样技术，近似和基于学习的方法来开发有效的风险评估和优化算法。具体而言，该项目做出以下技术贡献：1）用于移动边缘计算应用程序的现实性能和SLA模型的开发和验证，2）对三种风险措施的建模和优化（风险概率，价值，价值风险，价值和有条件的价值风险，以及单用户自动化的边缘资源策划和优化的单位型号和优化的单位纽扣，3）多用户多节点风险感知的边缘资源提供。所有研究成果将通过具有公共痕迹的测试床和/或大型模拟进行评估，并将在PIS的网站上公开提供，以促进结果繁殖和未来的研究进步。该奖项反映了NSF的法定任务，并通过基金会的知识优点和广泛的影响来评估NSF的法定任务。

项目成果

VeriEdge: Verifying and Enforcing Service Level Agreements for Pervasive Edge Computing

VeriEdge：验证和执行普适边缘计算的服务级别协议

• 发表时间: 2023
• 期刊: IEEE International Conference on Computer Communications (INFOCOM
• 作者: Wang, Xiaojian;Yu, Ruozhou;Yang, Dejun;Gu, Huayue;Li, Zhouyu
• 通讯作者: Li, Zhouyu

Principles and Practices for Application-Network Co-Design in Edge Computing

边缘计算应用网络协同设计的原则与实践

• DOI: 10.1109/mnet.128.2200430
• 发表时间: 2022
• 期刊: IEEE Network
• 影响因子: 9.3
• 作者: Yu, Ruozhou;Xue, Guoliang
• 通讯作者: Xue, Guoliang

Data-Driven Edge Resource Provisioning for Inter-Dependent Microservices with Dynamic Load

• DOI: 10.1109/globecom46510.2021.9685155
• 发表时间: 2021-12
• 期刊: 2021 IEEE Global Communications Conference (GLOBECOM)
• 作者: Ruozhou Yu;Szu-Yu Lo;Fangtong Zhou;G. Xue

FedAegis: Edge-Based Byzantine-Robust Federated Learning for Heterogeneous Data

• DOI: 10.1109/globecom48099.2022.10000981
• 发表时间: 2022-12
• 期刊: GLOBECOM 2022 - 2022 IEEE Global Communications Conference
• 作者: Fangtong Zhou;Ruozhou Yu;Zhouyu Li;Huayue Gu;Xiaojian Wang

Edge-Assisted Collaborative Perception in Autonomous Driving: A Reflection on Communication Design

自动驾驶中的边缘辅助协作感知：对通信设计的反思

• DOI: 10.1145/3453142.3491413
• 发表时间: 2021
• 期刊: ACM/IEEE Symposium on Edge Computing (SEC
• 作者: Yu, Ruozhou;Yang, Dejun;Zhang, Hao
• 通讯作者: Zhang, Hao