EDBT 2026 Demo / reviewers in the wild / expert
Danny Zhou
dblp:236/2854
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 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 · 91% Network measurement and analytics · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software-defined and programmable networks
network function virtualization |
0.4 | 1 | 2019 | Dynamic Network Function Instance Scaling Based on Traffic Forecasting and VNF Placement in Operator Data Centers · IEEE Trans. Parallel Distributed Syst. 2019 |
Software-defined and programmable networks › network function virtualization
virtual network function placement |
0.4 | 1 | 2019 | Dynamic Network Function Instance Scaling Based on Traffic Forecasting and VNF Placement in Operator Data Centers · IEEE Trans. Parallel Distributed Syst. 2019 |
Software-defined and programmable networks › network function virtualization
VNF scaling |
0.4 | 1 | 2019 | Dynamic Network Function Instance Scaling Based on Traffic Forecasting and VNF Placement in Operator Data Centers · IEEE Trans. Parallel Distributed Syst. 2019 |
Network measurement and analytics
traffic prediction |
0.1 | 1 | 2019 | Dynamic Network Function Instance Scaling Based on Traffic Forecasting and VNF Placement in Operator Data Centers · IEEE Trans. Parallel Distributed Syst. 2019 |
Methods — techniques the papers use, named apart from their topics
traffic forecasting · 0.4placement algorithms · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Dynamic Network Function Instance Scaling Based on Traffic Forecasting and VNF Placement in Operator Data CentersabstractTraffic in operator networks is time varying. Conventional network functions implemented by black-boxes should satisfy the peak traffic requirement, and hence result in low resource utilization. Thanks to the emergence of Virtual Network Function (VNF), which is realized by running networking software on Virtual Machines (VMs), the operator can dynamically scale in or scale out the VNF instances and hence save the required resources. In this paper, we introduce how the dynamic VNF scaling is implemented in practical operator Data Center Networks (DCNs). First, we analyze the traffic characteristics in our operator networks, and introduce how the VNFs are organized in a common operator DCN. Based on these backgrounds, we not only propose a traffic forecasting method, but also design two VNF placement algorithms to guide the dynamic VNF instance scaling. Through both the implementation in a real operator network and extensive real trace driven simulations, we demonstrate that our dynamic VNF instance scaling system can achieve higher service availability and save the VNF resources (e.g., CPU and memory) by up to 30 percent. Danny Zhou, Duan Chen |
IEEE Trans. Parallel Distributed Syst. | 2 |