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Wanli Gong

dblp:289/8351 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0003-1815-5919ORCID · 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
Edge and fog computing · 38% Software-defined and programmable networks · 38% Internet of things and sensor networks · 24%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Edge and fog computing
application placement
0.512021
NFV-Enabled IoT Service Provisioning in Mobile Edge Clouds · IEEE Trans. Mob. Comput. 2021
Internet of things and sensor networks › iot services
iot service provisioning
0.512021
NFV-Enabled IoT Service Provisioning in Mobile Edge Clouds · IEEE Trans. Mob. Comput. 2021
Edge and fog computing › mobile edge computing
mobile edge cloud
0.512021
NFV-Enabled IoT Service Provisioning in Mobile Edge Clouds · IEEE Trans. Mob. Comput. 2021
Software-defined and programmable networks
network function virtualization
0.512021
NFV-Enabled IoT Service Provisioning in Mobile Edge Clouds · IEEE Trans. Mob. Comput. 2021
Software-defined and programmable networks › network function virtualization
virtual network function placement
0.512021
NFV-Enabled IoT Service Provisioning in Mobile Edge Clouds · IEEE Trans. Mob. Comput. 2021

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

heuristic algorithm · 0.5approximation algorithm · 0.5
YearPublicationVenuePosition
2021 NFV-Enabled IoT Service Provisioning in Mobile Edge Clouds
abstract
Conventional Internet of Things (IoT) applications involve data capture from various sensors in environments, and the captured data then is processed in remote clouds. However, some critical IoT applications (e.g., autonomous vehicles) require a much lower response latency and more secure guarantees than those offered by remote clouds today. Mobile edge clouds (MEC) supported by the network function virtualization (NFV) technique have been envisioned as an ideal platform for supporting such IoT applications. Specifically, MECs enable to handle IoT applications in edge networks to shorten network latency, and NFV enables agile and low-cost network functions to run in low-cost commodity servers as virtual machines (VMs). One fundamental problem for the provisioning of IoT applications in an NFV-enabled MEC is where to place virtualized network functions (VNFs) for IoT applications in the MEC, such that the operational cost of provisioning IoT applications is minimized. In this paper, we first address this fundamental problem, by considering a special case of the IoT application placement problem, where the IoT application and VNFs of each service request are consolidated into a single location (gateway or cloudlet), for which we propose an exact solution and an approximation algorithm with a provable approximation ratio. We then develop a heuristic algorithm that controls the resource violation ratios of edge clouds in the network. For the IoT application placement problem for IoT applications where their VNFs can be placed to multiple locations, we propose an efficient heuristic that jointly places the IoT application and its VNFs. We finally study the performance of the proposed algorithms by simulations and implementations in a real test-bed, Experimental results show that the performance of the proposed algorithms outperform their counterparts by at least 10 percent.
Zichuan Xu, Wanli Gong, Qiufen Xia, Weifa Liang, Omer F. Rana, Guowei Wu 0001
IEEE Trans. Mob. Comput.2