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Shinae Woo

dblp:129/3030 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2018
—ORCID · none

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

Computer networks · 3 · 2 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
3 papers
Software-defined and programmable networks · 57% Transport protocols and congestion control · 21% Network measurement and analytics · 14%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%
Software engineering, system software, and programming languages
1 paper
Operating systems · 100%

Topics — the 8 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software-defined and programmable networks
network function virtualization
0.312018
Elastic Scaling of Stateful Network Functions · NSDI 2018
Software-defined and programmable networks › network function
stateful network functions
0.312018
Elastic Scaling of Stateful Network Functions · NSDI 2018
Cloud and datacenter computing
autoscaling
0.312018
Elastic Scaling of Stateful Network Functions · NSDI 2018
Transport protocols and congestion control › TCP
TCP implementation
0.212014
mTCP: a Highly Scalable User-level TCP Stack for Multicore Systems · NSDI 2014
Operating systems
network stack
0.212014
mTCP: a Highly Scalable User-level TCP Stack for Multicore Systems · NSDI 2014
Network measurement and analytics › mobile network measurement
cellular traffic characterization
0.212013
Comparison of caching strategies in modern cellular backhaul networks · MobiSys 2013
Content delivery and video streaming › caching › web caching
prefix caching
0.012013
Comparison of caching strategies in modern cellular backhaul networks · MobiSys 2013
Content delivery and video streaming › caching
web caching
0.012013
Comparison of caching strategies in modern cellular backhaul networks · MobiSys 2013

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

deep flow inspection · 0.2
YearPublicationVenuePosition
2018 Elastic Scaling of Stateful Network Functions
Shinae Woo, Justine Sherry, Sangjin Han, Sue B. Moon, Sylvia Ratnasamy, Scott Shenker
NSDI1
2014 mTCP: a Highly Scalable User-level TCP Stack for Multicore Systems
Eunyoung Jeong, Shinae Woo, Muhammad Asim Jamshed, Haewon Jeong, Sunghwan Ihm, Dongsu Han, KyoungSoo Park
NSDI2
2013 Comparison of caching strategies in modern cellular backhaul networks
abstract
Recent popularity of smartphones drives rapid growth in the demand for cellular network bandwidth. Unfortunately, due to the centralized architecture of cellular networks, increasing the physical backhaul bandwidth is challenging. While content caching in the cellular network could be beneficial, relatively few characteristics of the cellular traffic is known to come up with a highly-effetive caching strategy. In this work, we provide insight into flow and content-level characteristics of modern 3G traffic at a large cellular ISP in South Korea. We first develop a scalable deep flow inspection (DFI) system that can manage hundreds of thousands of concurrent TCP flows on a commodity multicore server. Our DFI system collects various HTTP/TCP-level statistics and produces logs for analyzing the effectiveness of conventional Web caching, prefix-based Web caching, and TCP-level redundancy elimination (RE) without a single packet drop at a 10~Gbps link. Our week-long measurements of over 370 TBs of the 3G traffic reveal that standard Web caching can reduce download bandwidth consumption up to 27.1% while simple TCP-level RE can save the bandwidth consumption up to 42.0% with a cache of 512~GB of RAM. We also find that applying TCP-level RE on the largest 9.4% flows eliminates 68.4% of the total redundancy. Most of the redundancy (52.1%~58.9%) comes from serving the same HTTP objects while the contribution by aliased URLs is up to 38.9%.
Shinae Woo, Eunyoung Jeong, Shinjo Park, Jong Min Lee 0001, Sunghwan Ihm, KyoungSoo Park
MobiSys1