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应用程序和移动应用程序的扩散由巨大的计算数据中心提供动力，其中包括多达数十万个具有分布在这些服务器上的数据的计算服务器。为了服务用户请求，这些服务器需要相互通信，这是数据中心网络促进的。随着数据中心的规模的增加，这些网络的设计和操作变得更加具有挑战性。今天的数据中心通常以非常低的利用方式运行，以满足用户的严格延迟要求。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建立服务器之间连接的算法的延迟性能。该分析将为开发的新算法提供较低的潜伏期和实施复杂性，同时改善利用率。该项目包括与公司互动以探索这些算法的使用，研究生的培训，通过本科和研究生课程传播这项研究，向高中生的宣传活动以及让本科生参与研究。该项目由两个主要部分组成。第一个开发了分析工具来研究数据中心网络的调度算法的性能。该领域的先前工作使用基于扩散限制和布朗近似值的方法，在研究数据中心网络时，这些方法变得笨拙。最近的工作证明了一种基于漂移的方法更简单的力量。该项目采用漂移方法来研究尾部潜伏期的性能，并将开发一种新型的矩函数方法来克服漂移方法的局限性。在第二部分中，这些工具将被利用以开发用于数据中心网络的新型低复杂性算法。这些算法的开发的重点是，除了文献中假定的独立和相同分布的随机到达之外，还可以适应更现实的交通模式。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准来通过评估来进行评估的。