TCP congestion control using reinforcement learning

TCP, a transport layer protocol which ensures the reliable delivery of information on the network, is the basis of Internet connectivity, with 85% of the worlds Internet traffic being TCP based. TCP however, is slow to adapt to changes in the network, drastically reducing the throughput at the first sign of possible congestion, thereby preventing rapid restoration of the throughput. Mitigating this problem has been a very active area of research, as, until recently, the idea of using Artificial Intelligence (AI) in this space was relatively limited. Recently, Reinforcement Learning (RL), a form of AI, has been explored in the networking space, and in enhancing the performance of TCP, this Thesis aims to expand the use of RL for TCP (TCP-CA/RL) in a software-defined data center. We demonstrate that our proposed approach is able to significantly reduce the impact of congestion on the end-to-end network throughput within the data center.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Karwani, Ali Hassan

Advisor dc:contributor.advisor

• Heydari, Shahram Shah

• Reinforcement learning
• OpenAI gym
• Software-defined networking (SDN)
• TCP congestion avoidance
• Network congestion

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1557

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1557