Enhancing Latency Reduction and Reliability for Internet Services with QUIC and WebRTC

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School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2024-09-23
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Date

2024

Department

Informaatio- ja tietoliikennetekniikan laitos
Department of Information and Communications Engineering

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Mcode

Degree programme

Language

en

Pages

71 + app. 59

Series

Aalto University publication series DOCTORAL THESES, 177/2024

Abstract

This dissertation introduces innovative systems and algorithms aimed at improving latency reduction and enhancing reliability for Internet services. To structure the research effectively, the dissertation categorizes the lifecycle of a service connection into three stages: service discovery, delivery, and migration. In the context of service discovery and migration, edge computing employs server replication to achieve low latency via user proximity and high reliability through load balancing. The primary challenges lie in developing a mechanism for rapid connection establishment and consistent optimal end-user mapping. A focal point of this study is the examination of QUIC and its potential to enhance service discovery and migration. By integrating QUIC's handshake data flow with conventional service discovery protocols, two systems are proposed to reduce the latency in connection setup and enhance the effectiveness of end-user mapping, with a particular emphasis on leveraging anycast and Domain Name System (DNS) technologies. For anycast, a novel approach in cloud computing is introduced to anycast routing, incorporating enhanced capabilities for name resolution and load awareness. In DNS, a middleware solution in the 5G core network improves performance, notably in query delay, cache hit rates, and consistency, thereby refining DNS-based discovery in edge cloud computing. Furthermore, along with essential server-side modifications, the solution extends QUIC's zero-round trip time (0-RTT) handshake feature to facilitate 0-RTT service migration, significantly boosting migration efficiency. Regarding service delivery, the data generation and transmission behavior is governed by the system and network capabilities. The challenge resides in designing a control algorithm to ensure consistent low-latency and reliable packet delivery, facilitating the application requirements. This dissertation concentrates on optimizing delivery control mechanisms within real-time video streaming, using WebRTC as the testbed. It provides an exhaustive analysis of how control parameters affect streaming performance and application metrics, leading to the development of an algorithm for optimizing the parameter setting during the slow start phase of congestion control. Furthermore, a machine learning based streaming control solution is proposed to jointly control multiple parameters, serving as a more general solution. This work also introduces an open-source framework designed to facilitate future research on applying machine learning to WebRTC control. Aiming to improve latency and reliability, this dissertation investigates the integration of emerging technologies such as QUIC, 5G, and machine learning within the established framework of edge computing. This research emphasizes the importance of cross-layer design in the optimization of Internet services, identifying machine learning as a promising approach.

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Supervising professor

Xiao, Yu, Prof., Aalto University, Department of Information and Communications Engineering, Finland

Thesis advisor

Cho, Byungjin, Dr., Nokia, Finland

Keywords

service discovery, cross-layer design, real-time video streaming

Other note

Parts

  • [Publication 1]: Xuebing Li, Yang Chen, Mengying Zhou, Tiancheng Guo, Chenhao Wang, Yu Xiao, Junjie Wan, and Xin Wang. Artemis: A Latency-Oriented Naming and Routing System. IEEE Transactions on Parallel and Distributed Systems, Volume: 33, Issue: 12, Pages: 4874 - 4890, December 2022.
    DOI: 10.1109/TPDS.2022.3207189 View at publisher
  • [Publication 2]: Xuebing Li, Byungjin Cho, Saimanoj Katta, Jose Costa Requena, and Yu Xiao. Aeacus: QUIC-powered Low-latency and Strong-consistency Name Resolution in 5G. Submitted to pre-review, 14 pages, February 2024
  • [Publication 3]: Xuebing Li, Byungjin Cho, and Yu Xiao. Balancing Latency and Accuracy on Deep Video Analytics at the Edge. In Proceedings of IEEE Annual Consumer Communications and Networking Conference, Pages: 299-306, January 2022.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202202232002
    DOI: 10.1109/CCNC49033.2022.9700636 View at publisher
  • [Publication 4]: Xuebing Li, Vikberg Esa, Byungjin Cho, and Yu Xiao. Pandia: Opensource Framework for DRL-based Real-time Video Streaming Control. In Proceedings of the ACM Multimedia Systems Conference, Pages 299–305, April 2024.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202405153338
    DOI: 10.1145/3625468.3652173 View at publisher

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https://urn.fi/URN:ISBN:978-952-64-1991-6

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[diss] Sähkötekniikan korkeakoulu / ELEC