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Zhouhao Wang

dblp:183/6704 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—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
Software-defined and programmable networks · 67% Network optimization and economics · 33%

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

TopicWeightPapersLastEvidence papers
Software-defined and programmable networks
openflow
0.212016
Engineering traffic uncertainty in the OpenFlow data plane · INFOCOM 2016
Software-defined and programmable networks
SDN traffic engineering
0.212016
Engineering traffic uncertainty in the OpenFlow data plane · INFOCOM 2016
Network optimization and economics
traffic uncertainty
0.212016
Engineering traffic uncertainty in the OpenFlow data plane · INFOCOM 2016

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

optimization · 0.2dual routing · 0.2
YearPublicationVenuePosition
2016 Engineering traffic uncertainty in the OpenFlow data plane
abstract
This paper is driven by a simple question of whether traffic engineering in Software Defined Networking (SDN) can react quickly to bursty and unpredictable changes in traffic demand. The key challenge is to strike a careful balance between the overhead (frequently involving the SDN controller) and performance (the degree of congestion measured as the maximum load and the balance between the minimum and the maximum loads). Exploiting OpenFlow (OF) features, quick shift of routing paths for unpredictable traffic bursty is the focal point of this work. It is achieved by using a dual routing scheme and letting the data plane to select the appropriate path in reacting to uncertainty in traffic load. The proposed work is called DUCE (Demand Uncertainty Configuration sElection). Further, we describe a traffic distribution model, an optimization solution that calculates congestion-free traffic distribution plan which guarantees that each switch can select one of the paths in a distributed way, and moreover, OF details about detaching the functionality of responding to the demand uncertainty from the control plane and delegating it to the data plane. Simulations are performed validating the efficiency of DUCE under various network scenarios.
Fei Chen 0009, Chunming Wu 0001, Xiaoyan Hong, Zhouhao Lu, Zhouhao Wang, Chang-Ting Lin
INFOCOM5