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Chun-Yun Chang

dblp:62/10609 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Content delivery and video streaming · 87% Network performance modeling · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Content delivery and video streaming › peer-to-peer streaming
chunk scheduling
0.112011
Striking the balance between content diversity and content importance in swarm-based P2P streaming · ICNP 2011
Content delivery and video streaming
peer-to-peer streaming
0.112011
Striking the balance between content diversity and content importance in swarm-based P2P streaming · ICNP 2011

Methods — techniques the papers use, named apart from their topics

simulation · 0.1dynamic strategy-switching · 0.1
YearPublicationVenuePosition
2011 Striking the balance between content diversity and content importance in swarm-based P2P streaming
abstract
During recent years, the success of live swarm-based P2P streaming system has been witnessed. Nevertheless, how to design an effective mechanism for mitigating video quality degradation in a lossy network environment is still not thoroughly resolved yet. Unlike conventional client-server paradigm, there is data availability problem in swarm-based P2P streaming system. If we directly conduct the importance-first scheduling strategy in swarm-based P2P streaming system, the serious content bottleneck for low priority chunks occurs, particularly when the population size is large. In this work, we propose a dynamic strategy-switching approach that combines the advantages of random scheduling and importance first scheduling to deal with the problem. Simulation results indicate that our approach not only provides better scheduling efficiency, but also is scalable even if population size is large.
Chun-Yun Chang, Cheng-Fu Chou, Ming-Hung Chen
ICNP1