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Shuaixin Yu

dblp:389/2346 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0008-3728-1172ORCID · reported

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

Computer networks · 1 · 1 since 2021

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 · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Performance modeling and evaluation · 100%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Content delivery and video streaming
video-on-demand
0.812024
Magpie: Improving the Efficiency of A/B Tests for Large Scale Video-on-Demand Systems · IMC 2024

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

simulation · 1.5a/b testing · 1.5
YearPublicationVenuePosition
2024 Magpie: Improving the Efficiency of A/B Tests for Large Scale Video-on-Demand Systems
abstract
With the exponential rise in video traffic, researchers and developers require more effective tools to validate the efficacy of designed algorithms for Video-on-Demand (VoD) system. However, traditional experimental platforms face two main challenges: a lack of realistic testing and the need for longer and significant effort. To overcome these limitations, we propose Magpie, an efficient experimental platform tailored for VoD systems. Magpie leverages a realistic operational setting, rapid testing, and high reproducibility to closely simulate online user environments without impacting production systems. Compared to conventional simulations, our evaluation demonstrates that Magpie reduces the disparity with online experiments by 85.6%. Deployed within our company-a leading video content provider in China-Magpie has efficiently validated over tens of algorithms, with 80% demonstrating enhanced performance in subsequent online tests.
Hebin Yu, Haiping Wang 0002, Chenfei Tian, Sandesh Dhawaskar Sathyanarayana, Shu Shi, Zhichen Xue, Shuaixin Yu, Yajie Peng, Xiaofei Pang
IMC7