EDBT 2026 Demo / reviewers in the wild / expert
Tianyi Ge
dblp:287/2997
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2026
0009-0003-6852-0432ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 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 architecture, parallel and distributed computing, and storage systems
1 paper |
Cloud and datacenter computing · 87% Embedded and real-time systems · 13% | |
| Computer networks
1 paper |
Edge and fog computing · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Edge and fog computing
edge server |
0.5 | 1 | 2021 | Joint Task Scheduling and Containerizing for Efficient Edge Computing · IEEE Trans. Parallel Distributed Syst. 2021 |
Edge and fog computing
task scheduling |
0.5 | 1 | 2021 | Joint Task Scheduling and Containerizing for Efficient Edge Computing · IEEE Trans. Parallel Distributed Syst. 2021 |
Cloud and datacenter computing › virtualization › containerization
container-based virtualization |
0.5 | 1 | 2021 | Joint Task Scheduling and Containerizing for Efficient Edge Computing · IEEE Trans. Parallel Distributed Syst. 2021 |
Cloud and datacenter computing
virtualization |
0.5 | 1 | 2021 | Joint Task Scheduling and Containerizing for Efficient Edge Computing · IEEE Trans. Parallel Distributed Syst. 2021 |
Embedded and real-time systems
real-time scheduling |
0.1 | 1 | 2021 | Joint Task Scheduling and Containerizing for Efficient Edge Computing · IEEE Trans. Parallel Distributed Syst. 2021 |
Methods — techniques the papers use, named apart from their topics
system modeling · 1.0simulation · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | U-Shaped WaveNet With Adaptive Volterra Neural Networks for Active Noise Control
Tianyi Ge, Ning Han 0008 |
IEEE Signal Process. Lett. | 1 |
| 2025 | Multi-Scale Dynamic Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting
Tianyi Ge, Longxi Feng |
ICIC (19) | 2 |
| 2021 | Joint Task Scheduling and Containerizing for Efficient Edge ComputingabstractContainer-based operation system (OS) level virtualization has been adopted by many edge-computing platforms. However, for an edge server, inter-container communications, and container management consume significant CPU resources. Given an application composed of interdependent tasks, the number of such operations is closely related to the dependency between the scheduled tasks. Thus, to improve the execution efficiency of an application in an edge server, task scheduling and task containerizing need to be considered together. To this end, a joint task scheduling and containerizing (JTSC) scheme is developed in this article. Experiments are first carried out to quantify the resource utilization of container operations. System models are then built to capture the features of task execution in containers in an edge server with multiple processors. With these models, joint task scheduling and containerizing is conducted as follows. First, tasks are scheduled without considering containerization, which results in initial schedules. Second, based on system models and guidelines gained from the initial schedules, several containerization algorithms are designed to map tasks to containers. Third, task execution durations are updated by adding the time for inter-container communications, and then the task schedules are updated accordingly. The JTSC scheme is evaluated through extensive simulations. The results show that it reduces inefficient container operations and enhances the execution efficiency of applications by 60 percent. Xiaochen Zhou, Tianyi Ge, Xudong Wang 0001, Taewon Hwang |
IEEE Trans. Parallel Distributed Syst. | 3 |