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Erfang Tian

dblp:182/1939 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

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

Computer networks · 1

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 · 77% Edge and fog computing · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Content delivery and video streaming
content replication
0.312017
Propagation- and Mobility-Aware D2D Social Content Replication · IEEE Trans. Mob. Comput. 2017

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

optimization · 0.3distributed algorithm · 0.3
YearPublicationVenuePosition
2017 Propagation- and Mobility-Aware D2D Social Content Replication
abstract
Mobile online social network services have seen rapid expansion; thus, the corresponding huge amounts of user-generated social media contents propagating between users via social connections have significantly challenged the traditional content delivery paradigm. First, replicating all the contents generated by users to edge servers that well “fit” the receivers becomes difficult due to limited bandwidth and storage capacities. Motivated by device-to-device (D2D) communication, which allows users with smart devices to transfer content directly, we propose replicating bandwidth-intensive social contents in a device-to-device manner. Based on large-scale measurement studies on social content propagation and user mobility patterns in edge-network regions, we observe the following: (1) Device-to-device replication can significantly help users download social contents from neighboring peers. (2) Both social propagation and mobility patterns affect how contents should be replicated. (3) The replication strategies depend on regional characteristics (e.g., how users move across regions). Using these measurement insights, we propose a propagationand mobility-aware content replication strategy for edge-network regions, in which social contents are assigned to users in edge-network regions according to a joint consideration of social graphs, content propagation, and user mobility. We formulate the replication scheduling as an optimization problem and design a distributed algorithm using only historical, local, and partial information to solve it. Trace-driven experiments further verify the superiority of our proposal: compared with conventional pure-movement-based and popularity-based approaches, our design can significantly improve (2 - 4-fold improvement) the amount of social content successfully delivered via device-to-device replication.
Zhi Wang 0001, Lifeng Sun, Miao Zhang 0003, Haitian Pang, Erfang Tian, Wenwu Zhu 0001
IEEE Trans. Mob. Comput.5