Demo Data from EDBT 2026 / reviewers in the wild / expert
Fangliang Lou
dblp:340/7402
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
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 |
Software-defined and programmable networks · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Performance modeling and evaluation · 77% Parallel and multicore computing · 23% |
Topics — the 3 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 |
0.7 | 1 | 2023 | Performance Tuning via Lean Measurements for Acceleration of Network Functions Virtualization · IEEE/ACM Trans. Netw. 2023 |
Software-defined and programmable networks › network function virtualization
NFV acceleration |
0.7 | 1 | 2023 | Performance Tuning via Lean Measurements for Acceleration of Network Functions Virtualization · IEEE/ACM Trans. Netw. 2023 |
Performance modeling and evaluation
performance tuning |
0.7 | 1 | 2023 | Performance Tuning via Lean Measurements for Acceleration of Network Functions Virtualization · IEEE/ACM Trans. Netw. 2023 |
Methods — techniques the papers use, named apart from their topics
lean measurement · 1.3debugger-based profiling · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Performance Tuning via Lean Measurements for Acceleration of Network Functions VirtualizationabstractNetwork Functions Virtualization (NFV) replaces the specialized hardware with the software-based forwarding to promise the flexibility, scalability and automation benefits. With an increasing range of applications, NFV must ultimately forward packets at rates that are comparable to the native and specialized hardware-based approaches. However, the transition packet forwarding from specialized hardware to software-based has turned out to be more challenging than expected. Thus, NFV acceleration is desperately needed to play a crucial role in the development of NFV. It is an interesting issue how to address the persistent performance tuning in a way that provides far greater flexibility to meet the demands of power. The existing developments are very inefficient, since that the uncontrollable and unanticipated performance regressions frequently occur. Besides, the environments for full system simulations are traditionally expensive and time consuming to evaluate the system performance. In this paper, we propose the methodology named as “NFV Acceleration via Lean Measurements (NALM)” to tune the performance for the NFV acceleration. NALM provides a holistic measurement approach through combining individual measures to quickly identify the bottlenecks, which can help developers with a better understanding of the design tradeoffs. Moreover, the environments for large scale performance simulation are replaced by a debugger. Thus, the waste is eliminated in terms of time consumption and infrastructure costs of the full system simulation. The systematic analysis of the multi-cores speedup ratio highlights the potential optimization space and rules. We further propose the improvement recommendations on efficient practices. The experiments evaluate the specific effects, and the relationship between the metrics and forwarding performance. Qiang Wu 0018, Xiangping Bryce Zhai, Chunming Wu 0001, Fangliang Lou, Hongke Zhang |
IEEE/ACM Trans. Netw. | 5 |