权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CRII: CIF: Resource Allocation in Data Center Networks: Algorithms, Fundamental Limits and Performance Bounds

CRII：CIF：数据中心网络中的资源分配：算法、基本限制和性能界限

基本信息

批准号：
1850439
负责人：
Siva Theja Maguluri
金额：
$ 17.5万
依托单位：
Georgia Tech Research Corporation
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：
2019-06-01 至 2022-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1850439&HistoricalAwards=false
关键词：
CRII CIF Resource Allocation Data

项目摘要

Today's era of big data and the proliferation of web apps and mobile apps is powered by enormous computing data centers, consisting of up to hundreds of thousands of computing servers with data distributed over these servers. In order to serve user requests, these servers need to communicate with each other, and this is facilitated by a data center network. Design and operation of these networks becomes more challenging as the size of the data centers increases. Today's data centers are typically operated at very low utilizations in order to meet stringent latency requirements of the users. The focus of this project is to develop analytical techniques to study the delay performance of algorithms that are used to make connections between the servers in a data-center network. This analysis will feed the development novel algorithms that maintain low latency and implementation complexity while improving utilization. The project includes engagement with companies to explore the use of these algorithms, training of graduate students, dissemination of this research through undergraduate and graduate courses, outreach activities to high-school students and involving undergraduate students in research. This project consists of two main parts. The first develops analytical tools to study the performance of scheduling algorithms for data-center networks. Prior work in this area used methods based on diffusion limits and Brownian approximations, which become unwieldy when studying data-center networks. More recent work demonstrates the power of a much simpler drift-based approach. This project employs the drift method to study the performance of tail latencies, and a novel moment-generating function method will be developed to overcome the limitations of the drift method. In the second part, these tools will be leveraged to develop novel low-complexity algorithms for data-center networks. A key focus in the development of these algorithms is accommodating more realistic traffic patterns beyond the independent and identically distributed random arrivals assumed in the literature.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.

当今的大数据时代以及Web应用程序和移动的应用程序的激增是由庞大的计算数据中心提供动力的，这些数据中心由多达数十万台计算服务器组成，数据分布在这些服务器上。为了满足用户的请求，这些服务器需要相互通信，而数据中心网络有助于实现这一点。随着数据中心规模的增加，这些网络的设计和运营变得更具挑战性。当今的数据中心通常以非常低的利用率运行，以满足用户严格的延迟要求。该项目的重点是开发分析技术，以研究用于在数据中心网络中的服务器之间建立连接的算法的延迟性能。该分析将为开发新算法提供支持，这些算法可保持低延迟和实施复杂性，同时提高利用率。该项目包括与公司合作，探索这些算法的使用，培训研究生，通过本科和研究生课程传播这项研究，向高中生开展外联活动，并让本科生参与研究。该项目包括两个主要部分。第一个开发分析工具，研究数据中心网络的调度算法的性能。这一领域的先前工作使用基于扩散极限和布朗近似的方法，在研究数据中心网络时变得笨拙。最近的工作证明了一个更简单的漂移为基础的方法的力量。本计画采用漂移法来研究尾喷管的性能，并将发展一种新的力矩母函数法来克服漂移法的局限性。在第二部分中，这些工具将被用来为数据中心网络开发新的低复杂度算法。在这些算法的发展的一个关键重点是容纳更现实的交通模式以外的独立和相同分布的随机arrivals假设在literature.This奖项反映了NSF的法定使命，并已被认为是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的支持。