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

许多物联网（IoT）应用，如自动驾驶汽车和增强现实，都需要将任务转移到更强大的计算基础设施——云。这些应用程序需要低延迟和快速响应时间，由于用户和云之间的物理距离和带宽限制，这通常很难实现。为了应对这些限制，边缘计算作为一种新的范例被引入，它优化云计算，在网络边缘提供分布式计算解决方案，物联网用户在其中利用其附近的计算资源（有时称为“cloudlets”）。在边缘计算需求激增的推动下，从环境和成本的角度来看，云、小云和设备的能耗变得越来越重要。现有的云计算能耗改进大多来自工程改进，而不是算法改进。这样的实践没有纳入云/cloudlets/设备可用的重要优化机会，以降低其能耗。这个为期两年的项目侧重于开发资源管理系统，以显着提高边缘计算的能源效率。该项目包括以下研究重点：1)通过探索重复的能源消耗模式和确定潜在的改进来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，引入能源感知数据和工作分解算法；3)基于匹配理论、图论和分布式在线算法设计，开发具有能量意识的边缘构件作业布置；4)在国家科学基金会资助的CloudLab平台上对系统进行评估。该项目将能够更有效地在边缘使用云计算，同时减少能源消耗，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。该项目可能会带来社会效益，如促进宜居社区和智慧城市。本研究将通过云计算课程整合到课堂教学中。这个项目的专门外联活动的目的是增加来自科学和工程领域代表性不足群体的学生的参与。该项目将维持一个专门的网站，以传播结果。该奖项反映了美国国家科学基金会的法定使命，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。

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.