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: 2007469
• 负责人: Guoliang Xue
• 金额: $ 14.25万
• 依托单位: Arizona 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=2007469&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.

移动的边缘计算的出现是为了解决在支持现代移动的应用时，云计算作为基于互联网的服务的长端到端延迟、低吞吐量和不可预测性。然而，缺乏服务水平协议（SLA）形式的性能保证会导致关键应用程序的性能下降，使其无法使用或不安全。由于移动的环境中的高动态性，边缘提供商可能会因提供可能被违反的SLA保证而承担重大的财务风险。这阻碍了边缘提供商在没有首先了解并能够控制相关风险的情况下提供SLA保证。该项目旨在开发工具，帮助边缘提供商量化和最大限度地减少与通过资源规划和编排提供边缘SLA保证关键性能指标相关的风险。该项目将大大提高我们对边缘计算风险因素的认识，并在边缘计算研究中开辟新的前沿。此外，该项目将实现和增强改变生活的边缘应用，如移动的视觉和自动驾驶，促进边缘计算行业的投资和加快发展，为未来的计算劳动力培养高素质的人才，通过课程开发和研究传播，扩大对边缘计算的认识和兴趣。该项目将奠定综合风险建模的理论和算法基础，提供边缘SLA保证的优化。该项目将移动的边缘应用的现实性能模型与投资组合管理中的风险管理理论相结合，并通过随机优化，凸优化，抽样技术，近似和基于学习的方法开发有效的风险评估和优化算法。具体而言，该项目做出了以下技术贡献：1）为移动的边缘计算应用开发和验证现实的性能和SLA模型，2）建模和优化三种风险度量（风险概率、风险价值和条件风险价值），用于单用户风险感知边缘资源编排，3）用于多用户单节点风险感知边缘资源编排的建模和优化，以及4）用于多用户多节点风险感知边缘资源供应的建模和优化。所有研究成果将通过试验台和/或大规模模拟进行评估，并公开在PI的网站上，以促进成果复制和未来的研究进展沿着路线。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Exploring Machine Learning Algorithms for User Activity Inference from IoT Network Traffic

• DOI: 10.1109/mass58611.2023.00052
• 发表时间: 2023-09
• 期刊: 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
• 作者: Kuai Xu;Yinxin Wan;Xuanli Lin;Feng Wang;Guoliang Xue

Entanglement Distribution in Satellite-Based Dynamic Quantum Networks

• DOI: 10.1109/mnet.2023.3321706
• 发表时间: 2023-06
• 期刊: IEEE Network
• 影响因子: 9.3
• 作者: A. Chang;Yinxin Wan;G. Xue;Arunabha Sen

EA-Market: Empowering Real-Time Big Data Applications with Short-Term Edge SLA Leases

• DOI: 10.1109/icccn58024.2023.10230160
• 发表时间: 2023-07
• 期刊: 2023 32nd International Conference on Computer Communications and Networks (ICCCN)
• 作者: Ruozhou Yu;Huayue Gu;Xiaojian Wang;Fangtong Zhou;G. Xue;Dejun Yang

Inferring User Activities from IoT Device Events in Smart Homes: Challenges and Opportunities

• DOI: 10.1109/icccn54977.2022.9868917
• 发表时间: 2022-07
• 期刊: 2022 International Conference on Computer Communications and Networks (ICCCN)
• 作者: Xuanli Lin;Yinxin Wan;Kuai Xu;Feng Wang;G. Xue

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