Applications of Reinforcement Learning in Data Center Networks

MPhil Thesis Defence


Title: "Applications of Reinforcement Learning in Data Center Networks"

By

Mr. Justinas LINGYS


Abstract

Data centers are at the core of most traditional online services such as 
the Web and email, and more recent cloud computing. With an increase in 
application agility and customers’ stringent requirements, data centers 
face demanding latency requirements to satisfy the variety of 
applications. Introducing an insignificant delay to user-facing 
applications may result in huge losses for data center operators and a 
waste of computing resources.

Networking delay is one of the major contributors to the latency issue, 
hence dealing with networking overheads is essential in order to satisfy 
data center operators’ targets. This thesis discusses methods of handling 
data center delays, investigates data flow scheduling as one of the 
methods, introduces machine learning with a focus on deep reinforcement 
learning, provides a discussion on deep learning applications in data 
centers, and proposes a data flow scheduling mechanism for data centers by 
exploiting the state-of-the-art deep reinforcement learning techniques.

The proposed flow scheduling system, AuTO, borrows contemporary ideas from 
deep reinforcement learning to schedule flows with an objective to 
minimize the average flow completion time. AuTO is distinct from other 
scheduling solutions as it adapts its decisions to match the current data 
traffic and improves with time.

Furthermore, AuTO demonstrates that deep reinforcement learning can be 
used to solve data center scale problems and that human heuristics-based 
data flow scheduling can benefit from feedback in dynamic environments.


Date:			Friday, 17 August 2018

Time:			9:30am - 11:30am

Venue:			Room 3494
 			Lifts 25/26

Committee Members:	Dr. Kai Chen (Supervisor)
 			Prof. Qian Zhang (Chairperson)
 			Dr. Ke Yi


**** ALL are Welcome ****