id:"swepub:oai:DiVA.org:kth-316104"
Search: id:"swepub:oai:DiVA.org:kth-316104" > Meta-reinforcement ...
- 1 of 1
- Previous record
- Next record
- To hitlist
Meta-reinforcement learning via buffering graph signatures for live video streaming events
-
- Antaris, Stefanos (author)
- Hive Streaming AB, Sweden
-
- Rafailidis, Dimitrios (author)
- University of Thessaly, Greece
-
- Girdzijauskas, Sarunas (author)
- KTH,Programvaruteknik och datorsystem, SCS
- (creator_code:org_t)
- 2022-01-19
- 2021
- English.
- In: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021. - New York, NY, USA : Association for Computing Machinery (ACM). ; , s. 385-392
- Conference paper (peer-reviewed)
Abstract
Subject headings
Close
- 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
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
Keyword
- 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
Publication and Content Type
- ref (subject category)
- kon (subject category)
To the university's database
- 1 of 1
- Previous record
- Next record
- To hitlist
Find more in SwePub
- By the author/editor
- Antaris, Stefano ...
- Rafailidis, Dimi ...
- Girdzijauskas, S ...
- About the subject
-
- NATURAL SCIENCES
- NATURAL SCIENCES
- and Computer and Inf ...
- and Software Enginee ...
- Articles in the publication
- Proceedings of t ...
- By the university
- Royal Institute of Technology
Search outside SwePub
- Extend your search to:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - National Library Systems
- LIBRIS.kb.se
