Demo Data from EDBT 2026 / reviewers in the wild / expert
Xindi He
dblp:378/3222
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2026
—ORCID · conflict
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 |
Vehicular, aerial and satellite networks · 50% Network optimization and economics · 50% | |
| Software engineering, system software, and programming languages
1 paper |
Services computing and microservices · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Vehicular, aerial and satellite networks › satellite networks
LEO satellite networks |
1.0 | 1 | 2026 | Throughput-Optimized Service Routing for Microservice Flows in LEO Satellite Networks · IEEE Trans. Mob. Comput. 2026 |
Network optimization and economics
service routing |
1.0 | 1 | 2026 | Throughput-Optimized Service Routing for Microservice Flows in LEO Satellite Networks · IEEE Trans. Mob. Comput. 2026 |
Services computing and microservices
service orchestration |
0.3 | 1 | 2026 | Throughput-Optimized Service Routing for Microservice Flows in LEO Satellite Networks · IEEE Trans. Mob. Comput. 2026 |
Methods — techniques the papers use, named apart from their topics
queue backlogs · 2.0distributed routing algorithm · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Multi-objective reinforcement learning for adaptive load balancing in cloud data centers
Xindi He, Ting Wang 0001 |
Comput. Networks | 1 |
| 2026 | Throughput-Optimized Service Routing for Microservice Flows in LEO Satellite NetworksabstractSatellite-based microservice systems have emerged as a promising architecture for enabling scalable and flexible service deployment in Low Earth Orbit (LEO) satellite networks, which are increasingly recognized as a key solution to meet the rising demand for global communication and computation, especially in remote and underserved regions. However, routing microservices efficiently in such systems presents major challenges due to the dynamic topology, intermittent connectivity, and unstable link conditions inherent to satellite constellations. These issues become even more severe under high traffic loads. To address these challenges, we propose Service Pressure, a novel routing algorithm specifically designed for satellite-based microservice systems. Service Pressure comprises two key components: first, the construction of an augmented subgraph to model the complex execution and data transmission dependencies of microservices; second, a distributed service routing algorithm that utilizes queue backlogs. This combination enables the algorithm to effectively handle high throughput and adapt to the dynamic network conditions of satellite constellations. By optimizing resource utilization, minimizing latency, and balancing load across satellite nodes, Service Pressure ensures efficient and stable service orchestration even under fluctuating traffic conditions. Extensive simulations demonstrate that our approach significantly outperforms existing routing methods, particularly in terms of throughput, latency, and stability. Service Pressure offers a significant advancement in satellite microservice routing, making it ideal for next-generation space-ground integrated networks. Xindi He, Ting Wang 0001, Yuanming Shi, Xin Liu 0049 |
IEEE Trans. Mob. Comput. | 1 |