자료유형  

 학위논문

Control Number  

0016933795

International Standard Book Number  

9798380319621

Dewey Decimal Classification Number  

005

Main Entry-Personal Name  

Swamy, Tushar.

Publication, Distribution, etc. (Imprint  

[S.l.] : Stanford University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(141 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

General Note  

Advisor: Shahbaz, Muhammad;Winstein, Keith.

Dissertation Note  

Thesis (Ph.D.)--Stanford University, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Network management is an increasingly difficult task for researchers and industry alike. Networks are growing rapidly in both scale and complexity. They now have to cater to a bigger application set and a larger user base than ever before while adhering to more and more stringent performance requirements. With so many challenges to running a network, operators must move beyond the era of hand-tuned algorithms and instead, adopt more automated approaches-i.e. AI-driven networks. In the search for more versatile tools in networks, many researchers have looked to machine learning (ML) as a vehicle for data-driven, adaptive mechanisms in networking systems. However, a number of pragmatic issues have plagued such development. Can we run ML in the packet path? Must operators build each new ML model by hand? How can we incorporate new data?In this dissertation, we show the construction of integral components required to build AI-driven networks. We first describe the design of Taurus, a platform to enable data-plane ML to run in the packet path of the network with a per-packet granularity, at line-rate. Furthermore, we demonstrate that the hardware for Taurus adds minimal overhead-less than 4% chip area and less than 3% power in our prototype. Next, we discuss Homunculus, a compiler stack for data-plane ML platforms (like Taurus) that allows for the automatic generation of resource and performance compliant ML models which outperform hand-tuned models by up to 16.9% in our tests. Finally, we show how these tools can be assembled to enable an adaptive ML loop in a network. Online labelling of raw data in the network can feed Homunculus, enabling the network to build new ML models from its own packet data. These models can then deploy learned policies in Taurus, setting the groundwork for upcoming AI-driven networks.

Subject Added Entry-Topical Term  

Operating systems.

Subject Added Entry-Topical Term  

Programming languages.

Subject Added Entry-Topical Term  

Deep learning.

Subject Added Entry-Topical Term  

Open source software.

Subject Added Entry-Topical Term  

Neural networks.

Subject Added Entry-Topical Term  

Labeling.

Subject Added Entry-Topical Term  

Flexibility.

Subject Added Entry-Topical Term  

Software upgrading.

Subject Added Entry-Topical Term  

Computer science.

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 85-03B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

로그인을 한후 보실 수 있는 자료입니다.

Control Number  

joongbu:643953