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Meta-reinforcement learning via buffering graph signatures for live video streaming events
Hive Streaming AB, Sweden.
University of Thessaly, Greece.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0003-4516-7317
2021 (English)In: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021, Association for Computing Machinery (ACM) , 2021, p. 385-392Conference paper, Published paper (Refereed)
Abstract [en]

In this study, we present a meta-learning model to adapt the predictions of the network's capacity between viewers who participate in a live video streaming event. We propose the MELANIE model, where an event is formulated as a Markov Decision Process, performing meta-learning on reinforcement learning tasks. By considering a new event as a task, we design an actor-critic learning scheme to compute the optimal policy on estimating the viewers' high-bandwidth connections. To ensure fast adaptation to new connections or changes among viewers during an event, we implement a prioritized replay memory buffer based on the Kullback-Leibler divergence of the reward/throughput of the viewers' connections. Moreover, we adopt a model-agnostic meta-learning framework to generate a global model from past events. As viewers scarcely participate in several events, the challenge resides on how to account for the low structural similarity of different events. To combat this issue, we design a graph signature buffer to calculate the structural similarities of several streaming events and adjust the training of the global model accordingly. We evaluate the proposed model on the link weight prediction task on three real-world datasets of live video streaming events. Our experiments demonstrate the effectiveness of our proposed model, with an average relative gain of 25% against state-of-the-art strategies. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/melanie

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM) , 2021. p. 385-392
Keywords [en]
graph signatures, meta-reinforcement learning, video streaming, Cell proliferation, Computer vision, Markov processes, Reinforcement learning, Global models, Graph signature, Live video streaming, Markov Decision Processes, Meta-learning models, Metalearning, Network Capacity, Structural similarity, Video-streaming
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:kth:diva-316104DOI: 10.1145/3487351.3490973Scopus ID: 2-s2.0-85124416909OAI: oai:DiVA.org:kth-316104DiVA, id: diva2:1689304
Conference
ASONAM '21: International Conference on Advances in Social Networks Analysis and Mining, Virtual Event, The Netherlands, November 8-11, 2021
Note

Part of ISBN 9781450391283

QC 20220822

Available from: 2022-08-22 Created: 2022-08-22 Last updated: 2022-11-02Bibliographically approved
In thesis
1. Enabling Enterprise Live Video Streaming with Reinforcement Learning and Graph Neural Networks
Open this publication in new window or tab >>Enabling Enterprise Live Video Streaming with Reinforcement Learning and Graph Neural Networks
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Over the last decade, video has vastly become the most popular way the world consumes content. Due to the increased popularity, video has been a strategic tool for enterprises. More specifically, enterprises organize live video streaming events for both internal and external purposes in order to attract large audiences and disseminate important information. However, streaming a high- quality video internally in large multinational corporations, with thousands of employees spread around the world, is a challenging task. The main challenge is to prevent catastrophic network congestion in the enterprise network when thousand of employees attend a high-quality video event simultaneously. Given that large enterprises invest a significant amount of their annual budget on live video streaming events, it is essential to ensure that the office network will not be congested and each viewer will have high quality of experience during the event.

To address this challenge, large enterprises employ distributed live video streaming solutions to distribute high-quality video content between viewers of the same network. Such solutions rely on prior knowledge of the enterprise network topology to efficiently reduce the network bandwidth requirements during the event. Given that such knowledge is not always feasible to acquire, the distributed solutions must detect the network topology in real-time during the event. However, distributed solutions require a service to detect the network topology in the first minutes of the event, also known as the joining phase. Failing to promptly detect the enterprise network topology negatively impacts the event’s performance. In particular, distributed solutions may establish connections between viewers of different offices with limited network capacity. As a result, the enterprise network will be congested, and the employees will drop the event from the beginning of the event if they experience video quality issues.

In this thesis, we investigate and propose novel machine learning models allowing the enterprise network topology service to detect the topology in real- time. In particular, we investigate the network distribution of live video streaming events caused by the distributed software solutions. In doing so, we propose several graph neural network models to detect the network topology in the first minutes of the event. Live video streaming solutions can adjust the viewers’ connections to distribute high-quality video content between viewers of the same office, avoiding the risk of network congestion. We compare our models with several baselines in real-world datasets and show that our models achieve significant improvement via empirical evaluations.

Another critical factor for the efficiency of live video streaming events is the enterprise network topology service latency. Distributed live video streaming solutions require minimum latency to infer the network topology and adjust the viewers’ connections. We study the impact of the graph neural network size on the model’s online inference latency and propose several knowledge distillation strategies to generate compact models. Therefore, we create models with significantly fewer parameters, reducing the online inference latency while achieving high accuracy in the network topology detection task. Compared with state-of-the-art approaches, our proposed models have several orders of magnitude fewer parameters while maintaining high accuracy.

Furthermore, we address the continuously evolving enterprise network topology problem. Modern enterprise networks frequently change their topology to manage their business needs. Therefore, distributed live video streaming solutions must capture the network topology changes and adjust their network topology detection service in real time. To tackle this problem, we propose several novel machine learning models that exploit historical events to assist the models in detecting the network topology in the first minutes of the event. We investigate the distribution of the viewers participating in the events. We propose efficient reinforcement learning and meta-learning techniques to learn the enterprise network topology for each new event. By applying meta-learning and reinforcement learning, we can generalize network topology changes and ensure that every viewer will have a high-quality experience during an event. Compared with baseline approaches, we achieved superior performance in establishing connections between viewers of the same office in the first minutes of the event. Therefore, we ensure that distributed solutions provide a high return on investment in every live video streaming event without risking any enterprise network congestion. 

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2022
Series
TRITA-EECS-AVL ; 2022:69
Keywords
Graph Neural Networks, Reinforcement Learning, Meta-Learning, Knowledge Distillation, Enterprise Liveo Video Streaming
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-320919 (URN)978-91-8040-398-6 (ISBN)
Public defence
2022-11-21, SAL-C, Electrum building, Kistagången 16, Stockholm, 09:00 (English)
Opponent
Supervisors
Note

QC 20221102

Available from: 2022-11-02 Created: 2022-11-02 Last updated: 2022-11-18Bibliographically approved

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Girdzijauskas, Sarunas

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