CNS: Small: Towards Intelligent, Coordinated and Scalable Management of Server Sprinting in Edge Data Centers

CNS：小型：迈向边缘数据中心服务器冲刺的智能、协调和可扩展管理

基本信息

批准号：
1910208
负责人：
Shaolei Ren
金额：
$ 38.02万
依托单位：
University of California-Riverside
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 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910208&HistoricalAwards=false
关键词：
CNS Small Towards Intelligent Coordinated

项目摘要

Built at numerous distributed locations in proximity to end users, edge data centers are essential ingredients of the next-generation computing paradigm to enable ultra-low latency- for emerging services such as augmented reality and assisted driving. Because of high capital expenses and/or limited physical space, however, the infrastructure capacity of edge data centers is often severely constrained and difficult to scale up for accommodating the constantly growing demand. To overcome the capacity constraint without compromising data center availability, this project exploits two important resources --- distributed energy storage devices (such as battery units) and cold air thermal energy storage --- and transforms them into server sprinting enablers for delivering the best service quality. The key contribution of this project is an intelligent and scalable framework based on state-of-the-art reinforcement learning, which holistically coordinates servers' sprinting decisions on the fly for maximizing the system-wide performance in highly dynamic and practical environments. This project considers two complementary types of edge data centers: those where the physical infrastructure and servers are managed by a single entity; and those where different tenants house their own servers and the physical infrastructure is controlled by the data center operator.This project can push the performance beyond the currently achievable limit in edge data centers, translating into better service quality and higher user satisfaction without costly infrastructure capacity expansion. It extends the exploration of computer system optimization to a new paradigm where scalable online learning is the core, and thus can catalyze a shift in future system designs. This project also contains a significant educational component and provides broad opportunities to attract a diverse population of students.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.

边缘数据中心建在靠近最终用户的众多分布式位置，是下一代计算范式的重要组成部分，可为增强现实和辅助驾驶等新兴服务实现超低延迟。然而，由于高昂的资本支出和/或有限的物理空间，边缘数据中心的基础设施容量通常受到严重限制，并且难以扩展以满足不断增长的需求。为了在不影响数据中心可用性的情况下克服容量限制，该项目利用了两种重要资源——分布式储能设备（例如电池单元）和冷空气热能存储——并将它们转化为服务器冲刺推动者，以提供最佳的服务。服务质量。该项目的关键贡献是基于最先进的强化学习的智能且可扩展的框架，它全面协调服务器的动态冲刺决策，以在高度动态和实际的环境中最大化系统范围的性能。该项目考虑两种互补类型的边缘数据中心：物理基础设施和服务器由单个实体管理的边缘数据中心；不同租户拥有自己的服务器，物理基础设施由数据中心运营商控制。该项目可以将性能提升到边缘数据中心目前可实现的极限之外，转化为更好的服务质量和更高的用户满意度，而无需昂贵的基础设施容量扩张。它将计算机系统优化的探索扩展到以可扩展在线学习为核心的新范式，从而可以催化未来系统设计的转变。该项目还包含重要的教育内容，并提供广泛的机会来吸引不同群体的学生。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

期刊论文数量（17）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

On the Latency Variability of Deep Neural Networks for Mobile Inference

关于移动推理深度神经网络的延迟可变性

DOI：
发表时间：
2020-06
期刊：
Workshop on Hot Topics in Edge Computing (1-page Poster
影响因子：
0
作者：
Yang, Luting;Lu, Bingqian;Ren, Shaolei
通讯作者：
Ren, Shaolei

Learning for Robust Combinatorial Optimization: Algorithm and Application

鲁棒组合优化的学习：算法与应用

DOI：
10.1109/infocom48880.2022.9796715
发表时间：
2022-05
期刊：
IEEE Conference on Computer Communications
影响因子：
0
作者：
Shao, Zhihui;Yang, Jianyi;Shen, Cong;Ren, Shaolei
通讯作者：
Ren, Shaolei

One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search

一台代理设备足以进行硬件感知神经架构搜索

DOI：
10.1145/3491046
发表时间：
2021-12
期刊：
Proceedings of the ACM on Measurement and Analysis of Computing Systems
影响因子：
0
作者：
Lu, Bingqian;Yang, Jianyi;Jiang, Weiwen;Shi, Yiyu;Ren, Shaolei
通讯作者：
Ren, Shaolei

Automating Deep Neural Network Model Selection for Edge Inference

自动进行边缘推理的深度神经网络模型选择

DOI：
10.1109/cogmi48466.2019.00035
发表时间：
2019-12
期刊：
IEEE First International Conference on Cognitive Machine Intelligence (CogMI
影响因子：
0
作者：
Lu, Bingqian;Yang, Jianyi;Chen, Lydia Y.;Ren, Shaolei
通讯作者：
Ren, Shaolei

Robustified Learning for Online Optimization with Memory Costs

具有内存成本的在线优化的稳健学习