Yu Gan

1.2k total citations
11 papers, 390 citations indexed

About

Yu Gan is a scholar working on Computer Networks and Communications, Information Systems and Control and Systems Engineering. According to data from OpenAlex, Yu Gan has authored 11 papers receiving a total of 390 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Networks and Communications, 9 papers in Information Systems and 1 paper in Control and Systems Engineering. Recurrent topics in Yu Gan's work include Software System Performance and Reliability (9 papers), Cloud Computing and Resource Management (9 papers) and IoT and Edge/Fog Computing (7 papers). Yu Gan is often cited by papers focused on Software System Performance and Reliability (9 papers), Cloud Computing and Resource Management (9 papers) and IoT and Edge/Fog Computing (7 papers). Yu Gan collaborates with scholars based in United States, China and United Kingdom. Yu Gan's co-authors include Christina Delimitrou, Yanqi Zhang, Meghna Pancholi, Dailun Cheng, Yuan He, David Lo, Peng Guo, Jiesheng Wu, Abhishek Dhanotia and David Infield and has published in prestigious journals such as Renewable Energy, IEEE Micro and ACM SIGOPS Operating Systems Review.

In The Last Decade

Yu Gan

10 papers receiving 378 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yu Gan United States	6	361	284	64	25	13	11	390
Shutian Luo Macao	6	266 0.7×	230 0.8×	43 0.7×	12 0.5×	10 0.8×	10	298
Anupam Chanda United States	9	301 0.8×	201 0.7×	50 0.8×	47 1.9×	21 1.6×	19	327
Marcelo Pasin Switzerland	8	207 0.6×	180 0.6×	68 1.1×	16 0.6×	16 1.2×	27	257
Yasuhiko Kanemasa Japan	12	343 1.0×	273 1.0×	39 0.6×	27 1.1×	15 1.2×	29	357
Miguel Matos Portugal	8	196 0.5×	148 0.5×	43 0.7×	15 0.6×	12 0.9×	40	241
Anwesha Das United States	6	305 0.8×	243 0.9×	65 1.0×	9 0.4×	37 2.8×	13	336
Amol Nayate United States	9	403 1.1×	153 0.5×	27 0.4×	24 1.0×	22 1.7×	13	414
Marvin McNett United States	4	205 0.6×	138 0.5×	12 0.2×	32 1.3×	14 1.1×	4	215
Louis Rilling France	5	245 0.7×	247 0.9×	16 0.3×	11 0.4×	13 1.0×	9	265
Zhe Wu United States	7	301 0.8×	248 0.9×	24 0.4×	14 0.6×	28 2.2×	14	343

Countries citing papers authored by Yu Gan

Since Specialization

Citations

This map shows the geographic impact of Yu Gan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yu Gan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu Gan more than expected).

Fields of papers citing papers by Yu Gan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yu Gan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yu Gan. The network helps show where Yu Gan may publish in the future.

Co-authorship network of co-authors of Yu Gan

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Gan. A scholar is included among the top collaborators of Yu Gan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yu Gan. Yu Gan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Gan, Yu, et al.. (2023). Sleuth: A Trace-Based Root Cause Analysis System for Large-Scale Microservices with Graph Neural Networks. 324–337. 4 indexed citations

2.

Gan, Yu, Yueying Li, Carlos Arturo Torres Gastelú, et al.. (2023). Ditto: End-to-End Application Cloning for Networked Cloud Services. 222–236. 7 indexed citations

3.

Gan, Yu, et al.. (2022). Abnormal Wind Turbine Data Identification Using a Dirichlet Process Gaussian Mixture Model. 2022 34th Chinese Control and Decision Conference (CCDC). 40. 529–534. 1 indexed citations

4.

Gan, Yu, et al.. (2022). Enabling Practical Cloud Performance Debugging with Unsupervised Learning. ACM SIGOPS Operating Systems Review. 56(1). 34–41.

5.

Guo, Peng, Yu Gan, & David Infield. (2022). Wind turbine performance degradation monitoring using DPGMM and Mahalanobis distance. Renewable Energy. 200. 1–9. 4 indexed citations

6.

Gan, Yu, et al.. (2022). Practical and Scalable ML-Driven Cloud Performance Debugging With Sage. IEEE Micro. 42(4). 27–36. 4 indexed citations

7.

Gan, Yu, et al.. (2021). Sage: practical and scalable ML-driven performance debugging in microservices. 135–151. 104 indexed citations

8.

Zhang, Yanqi, Yu Gan, & Christina Delimitrou. (2019). µqSim: Enabling Accurate and Scalable Simulation for Interactive Microservices. 14. 212–222. 8 indexed citations

9.

Gan, Yu, Yanqi Zhang, Dailun Cheng, et al.. (2019). Seer. 19–33. 174 indexed citations

10.

Gan, Yu, Yanqi Zhang, Dailun Cheng, et al.. (2019). Leveraging Deep Learning to Improve Performance Predictability in Cloud Microservices with Seer. ACM SIGOPS Operating Systems Review. 53(1). 34–39. 6 indexed citations

11.

Gan, Yu & Christina Delimitrou. (2018). The Architectural Implications of Cloud Microservices. IEEE Computer Architecture Letters. 17(2). 155–158. 78 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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