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Chang-Lin Ye

dblp:417/9732 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2 · 2 first-author · 2 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 · 83% Cellular and mobile networks · 17%

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

TopicWeightPapersLastEvidence papers
Edge and fog computing › mobile edge computing
computation offloading
0.912025
Mixed-Timescale Service Caching, Computing, and Communication Optimization for Low-Latency High-Reliability Edge-Cloud Networks · IEEE Trans. Mob. Comput. 2025
Edge and fog computing › edge-cloud computing
edge-cloud networks
0.912025
Mixed-Timescale Service Caching, Computing, and Communication Optimization for Low-Latency High-Reliability Edge-Cloud Networks · IEEE Trans. Mob. Comput. 2025
Edge and fog computing › mobile edge computing
service caching
0.912025
Mixed-Timescale Service Caching, Computing, and Communication Optimization for Low-Latency High-Reliability Edge-Cloud Networks · IEEE Trans. Mob. Comput. 2025
Cellular and mobile networks
5g
0.312025
Mixed-Timescale Service Caching, Computing, and Communication Optimization for Low-Latency High-Reliability Edge-Cloud Networks · IEEE Trans. Mob. Comput. 2025
Cellular and mobile networks › low-latency communication
ultra-reliable low-latency communication
0.312025
Mixed-Timescale Service Caching, Computing, and Communication Optimization for Low-Latency High-Reliability Edge-Cloud Networks · IEEE Trans. Mob. Comput. 2025

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

mixed timescale optimization · 0.9
YearPublicationVenuePosition
2025 Dynamic Service Caching, Computing, and Communication Resource Optimization for Low-Latency Edge-Cloud Networks
abstract
This work investigates the dynamic service caching, computing, and communication (3C) resource optimization for edge-cloud networks, where users, edge node, and cloud all having 3C capabilities. Based on the developed hierarchical network model, we propose a two-timescale problem aiming to minimize energy consumption under queue stability and latency and reliability constraints. A 3C resource optimization approach is then developed based on the formulated problem and Lyapunov optimization. Simulation results show that our approach can outperform the reference schemes in terms of average service delay and reliability, and can effectively provide tradeoff between energy consumption and service delay.
Chang-Lin Ye, Ming-Chun Lee, Chen-Yuan Wu
ICC1
2025 Mixed-Timescale Service Caching, Computing, and Communication Optimization for Low-Latency High-Reliability Edge-Cloud Networks
Chang-Lin Ye, Ming-Chun Lee, Chen-Yuan Wu
IEEE Trans. Mob. Comput.1