CRII: CSR: Energy-Aware Resource Management for Edge Computing: An Algorithmic Perspective

CRII：CSR：边缘计算的能源感知资源管理：算法视角

• 批准号: 1755913
• 负责人: Lena Mashayekhy
• 金额: $ 17.5万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755913&HistoricalAwards=false
• 关键词: CRII; CSR; Energy; Aware; Resource

Many Internet-of-Things (IoT) applications such as autonomous vehicles and augmented reality require offloading tasks to a more powerful computing infrastructure--cloud. These applications require low latency and fast response time, which are often difficult to attain due to the physical distance and bandwidth limitations between users and the cloud. To cope with these limitations, edge computing has been introduced as a new paradigm that optimizes cloud computing to provide distributed computing solutions at the edge of the network, where IoT users utilize the computing resources in their vicinity (sometimes called "cloudlets"). Driven by the surging demand for edge computing, the energy consumption of clouds, cloudlets, and devices becomes increasingly important both from an environmental and cost viewpoint. Most of the existing energy consumption improvements in clouds come from improved engineering rather than improved algorithms. Such a practice fails to incorporate significant optimization opportunities available for clouds/cloudlets/devices to reduce their energy consumption. This two-year project focuses on developing resource management systems to significantly improve energy efficiency in edge computing. The project consists of the following research thrusts: 1) understanding energy consumption in edge computing by exploring recurrent energy consumption patterns and identifying potential improvements; 2) introducing energy-aware data and job decomposition algorithms by developing mathematical and scalable computational models that seamlessly integrate multiple system objectives; 3) developing energy-aware placement of jobs to the edge components based on matching theory, graph theory, and distributed online algorithm design; and 4) evaluating the system on the National Science Foundation-funded CloudLab platform.This project will enable more efficient use of cloud computing at the edge, while reducing energy consumption, which leads to cost reduction in edge services. It also benefits IoT users by extending battery lifetime of their smart devices and by improving their application performance. This project may lead to societal benefits such as promoting livable communities and smart cities. This research will be integrated into the into classroom teaching via a cloud computing course. Specialized outreach activities of this project are aimed at increasing participation of students from groups underrepresented in science and engineering. The project will maintain a dedicated website for dissemination of results.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.

许多物联网(IoT)应用，如自动驾驶汽车和增强现实，都需要将任务卸载到更强大的计算基础设施--云。这些应用需要低延迟和快速响应时间，但由于用户和云之间的物理距离和带宽限制，通常很难实现这一点。为了应对这些限制，边缘计算作为一种新的范式被引入，它优化了云计算，以提供网络边缘的分布式计算解决方案，物联网用户在网络边缘利用他们附近的计算资源(有时称为云小程序)。在对边缘计算需求激增的推动下，从环境和成本的角度来看，云、云小程序和设备的能源消耗变得越来越重要。云中现有的大部分能耗改进来自于改进的工程，而不是改进的算法。这种做法未能包含可用于云/云小程序/设备的重大优化机会，以降低其能源消耗。这个为期两年的项目专注于开发资源管理系统，以显著提高边缘计算的能源效率。该项目包括以下研究内容：1)通过探索循环能源消耗模式和确定潜在的改进措施，了解边缘计算中的能源消耗；2)通过开发无缝集成多个系统目标的数学和可扩展计算模型，引入节能数据和作业分解算法；3)基于匹配理论、图论和分布式在线算法设计，开发节能作业到边缘组件的布局；4)在国家科学基金资助的CloudLab平台上对系统进行评估。该项目将使云计算在边缘更有效地使用，同时降低能源消耗，从而降低边缘服务的成本。它还通过延长智能设备的电池寿命和提高应用程序性能使物联网用户受益。这个项目可能会带来社会效益，比如促进宜居社区和智慧城市。这项研究将通过云计算课程融入课堂教学。该项目的专门外联活动旨在增加科学和工程专业任职人数不足的群体的学生的参与。该项目将维护一个专门的网站来发布结果。这一奖项反映了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审查标准进行评估，被认为值得支持。

项目成果

QoS-Aware Matching of Edge Computing Services to Internet of Things

• DOI: 10.1109/pccc.2018.8711148
• 发表时间: 2018-11
• 期刊: 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC)
• 作者: Nafiseh Sharghivand;F. Derakhshan;Lena Mashayekhy

Mobility-aware computation offloading in edge computing using prediction

• DOI: 10.1109/icfec50348.2020.00015
• 发表时间: 2020-05
• 期刊: 2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC)
• 作者: E. Maleki;Lena Mashayekhy

Generalized Cost-Aware Cloudlet Placement for Vehicular Edge Computing Systems

用于车辆边缘计算系统的通用成本感知 Cloudlet 放置

• DOI: 10.1109/cloudcom.2019.00033
• 发表时间: 2019
• 期刊: 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom
• 作者: Bhatta, Dixit;Mashayekhy, Lena
• 通讯作者: Mashayekhy, Lena

An Edge Computing Matching Framework With Guaranteed Quality of Service

• DOI: 10.1109/tcc.2020.3005539
• 发表时间: 2022-07
• 期刊: IEEE Transactions on Cloud Computing
• 影响因子: 6.5
• 作者: Nafiseh Sharghivand;F. Derakhshan;Lena Mashayekhy;Leyli Mohammad-Khanli

Privacy-by-Design Distributed Offloading for Vehicular Edge Computing

• DOI: 10.1145/3344341.3368804
• 发表时间: 2019-12
• 期刊: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing
• 作者: Weibin Ma;Lena Mashayekhy