Segment Routing based Traffic Engineering

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master_Javid_Sepehr_2023.pdf (1.44 MB)

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Perustieteiden korkeakoulu | Master's thesis
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Date

2023-08-21

Department

Major/Subject

Security and Cloud Computing

Mcode

SCI3113

Degree programme

Master’s Programme in Security and Cloud Computing (SECCLO)

Language

en

Pages

77

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Abstract

In modern networks, the increasing volume of network traffic and the diverse range of services with varying requirements necessitate the implementation of more advanced routing decisions and traffic engineering. This academic study proposes a QoS adaptive mechanism called ”Sepitto”, which utilizes Segment routing protocols, specifically SRv6, to address network-traffic control and congestion avoidance. Sepitto leverages data-plane traffic to convey Linux Qdisc statistics, such as queue size, packet drops, and buffer occupancy, in each Linux-based virtual router. By incorporating this information, edge routers become aware of the current network status, enabling them to make informed decisions regarding traffic paths based on QoS classes. SRv6 is employed to direct traffic along desired paths, avoiding congested links and minimizing queuing delays and overall latency. Moreover, Sepitto offers network administrators an interface to customize decision-making processes based on their policies, assigning costs to network graph edges by associating the provided statistics to a certain cost. To incorporate these costs, the implementation employs the Dijkstra algorithm to determine the path with the lowest cost. Performance analysis of Sepitto reveals minimal overhead compared to traditional routing methods, while effectively mitigating network congestion. The results demonstrate that Sepitto reduces traffic round-trip time during congestion while maintaining differentiated treatment for various QoS classes.

Description

Supervisor

Ylä-Jääski, Antti

Thesis advisor

Komu, Miika
Scazzariello, Mariano

Keywords

segment routing with IPv6, quality of service, congestion, extended Berkeley packet filter

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https://urn.fi/URN:NBN:fi:aalto-202308275152

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[dipl] Perustieteiden korkeakoulu / SCI