Xiangbo Tian

Wuhan University · China

Unclaimed · AI-structured profileOpenAlex

Wuhan University researcher applying deep reinforcement learning to cloud task and microservice scheduling

papers here

reviewed by Evolve

first-author

corresponding

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works · OpenAlex

h-index · OpenAlex

Bibliometric figures come from OpenAlex (2026-10-15) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.

Research profile

Output—

No output score is computed: h-index and i10-index are 0 in the OpenAlex snapshot and citation coverage is too thin. This axis would measure publication volume and per-year output; the three 2026 works are currently the only signal.

Impact—

No impact score is available: with h-index and i10-index both recorded as 0, there is no citation signal to assess influence. This axis would measure citation accumulation, citation quality, and uptake of the two 2026 preprints once indexed.

Focus0/100

The computed focus score is 0. The underlying field distribution splits evenly at 0.5 and 0.5 between two nearly identical cloud-computing scheduling labels, which collapses the concentration index, while topics also list Energy Efficiency in Computing (count 2) plus Topic Modeling, NLP Techniques, and Reinforcement Learning in Robotics (1 each). Read substantively, the reviewed work stays tightly centered on learning-based cloud scheduling.

Collaboration32/100

The collaboration score is 32, reflecting a small network: the OpenAlex-derived snapshot lists 4 distinct coauthors, with Tiangang Li, Shi Ying, Chuan Shi, and Ding Xiao recurring. On the two reviewed 2026 papers Tian is a non-corresponding author — listed last on HiGFRL and in a middle position on MCRL2 — suggesting work within a compact Wuhan University team rather than wide cross-institution collaboration; the low score mainly reflects the small distinct-coauthor count in the snapshot.

Activity40/100

The activity score is 40. The snapshot's counts-by-year contains only 2026, with 3 works, and recent3yWorks is 3 — the visible record is a short, recent burst rather than a sustained multi-year cadence. Both catalog papers are 2026 arXiv preprints, consistent with an early-stage publishing profile whose activity level cannot yet be judged over a longer window.

Reproducibility55/100

The reproducibility score is 55, a mean taken over 2 reviewed papers, both tagged medium readiness. Evolve's review notes that HiGFRL ships solid design and ablations but no variance testing and simulation-only evaluation, while MCRL2 reports single-run results in trace-driven simulation with a shaky cross-node claim. Methods, baselines, and ablation detail are therefore present, but statistical rigor and real-cluster validation are the main gaps.

Research summary

Xiangbo Tian is a researcher at Wuhan University whose indexed work centers on learning-based scheduling for cloud computing systems. The OpenAlex-derived snapshot shows a compact, early-stage record: 3 works recorded in 2026 (recent3yWorks = 3), h-index and i10-index of 0, and 4 distinct coauthors. Two 2026 arXiv preprints are reviewed in the Evolve catalog, both reinforcement-learning schedulers: HiGFRL couples hierarchical graph fusion — hypergraph plus multi-view representations — with an actor-critic (PPO) learner for dependency-aware DAG task scheduling in heterogeneous clouds, while MCRL2 augments soft actor-critic with a multi-resource cross-attention module for cloud microservice scheduling. Evolve's verdicts are constructive on both: solid design and ablations for HiGFRL, baseline-beating results for MCRL2, with shared reservations about statistical rigor — no variance testing, single-run results — and simulation-only evaluation. Topic tags list Cloud Computing and Resource Management (2) and Energy Efficiency in Computing (2), plus single counts for Topic Modeling, NLP Techniques, and Reinforcement Learning in Robotics, but the reviewed output stays firmly in cloud scheduling. The computed focus score of 0 reflects an even 0.5/0.5 split between two near-identical cloud-scheduling field labels rather than genuine dispersion. Collaboration (32) and activity (40) mark a small, recent, team-embedded record, while reproducibility scores 55, the mean over 2 reviewed papers.

Collaboration pattern

The snapshot lists 4 distinct coauthors, with Tiangang Li, Shi Ying, Chuan Shi, and Ding Xiao as recurring collaborators, giving a computed collaboration score of 32. Both reviewed papers are team-authored arXiv preprints with Xiangbo Tian as a non-corresponding author — last position on HiGFRL, middle on MCRL2 — a pattern consistent with contribution inside one Wuhan University group rather than independent or cross-institution projects. The small coauthor base, not absence of teamwork, drives the low score.

Research keywords

cloud computing systemsDAG task schedulingdeep reinforcement learningheterogeneous cloud schedulinggraph neural networkshypergraph convolutionactor-critic methods (PPO)soft actor-critic (SAC)microservice schedulingcross-attentionload balancingenergy-efficient computing

Highlights

HiGFRL: hypergraph-fused DRL for DAG scheduling on heterogeneous clouds
Reviewed by Evolve as a hierarchical graph fusion-driven reinforcement learning scheduler — hypergraph plus multi-view representations with an actor-critic (PPO) learner — for dependency-aware DAG task scheduling in heterogeneous cloud environments. The verdict credits solid design and ablation studies, while flagging the absence of variance testing and simulation-only evaluation.
MCRL2: cross-attention-augmented SAC for cloud microservice scheduling
A multi-resource cross-attention representation module augments soft actor-critic for microservice scheduling in cloud environments. Evolve's review finds it outperforms deep RL baselines in trace-driven simulation, but notes that results come from a single run and that the cross-node scalability claim is not yet firmly supported.

Timeline

2026HiGFRL preprint released on arXiv: hierarchical graph fusion-driven reinforcement learning for dependency-aware task scheduling in heterogeneous cloud, with Tian in the last author position.
2026MCRL2 preprint released on arXiv: multi-resource cross-attention-augmented reinforcement learning for cloud microservice scheduling, with Tian in a middle author position.
2026OpenAlex snapshot records 3 works in 2026 with 4 distinct coauthors — the entire indexed output window to date.

Papers in the Evolve catalog

Frequent co-authors

Tiangang Li×2 Shi Ying×2 Chuan Shi×1 Ding Xiao×1

About this profile

This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.

Bibliometrics are derived from OpenAlex only; no ORCID is linked, so this profile is name-resolved from public bibliographic data and may merge or split records for a common Chinese name.
Output and impact dimensions are unscored because h-index and i10-index are 0 in the snapshot and citation coverage is too thin; the two catalog papers are 2026 arXiv preprints, not verified peer-reviewed versions.
Reproducibility (55) averages only 2 reviewed papers — a small sample; activity and focus scores likewise rest on a single snapshot year (2026).
Career facts, awards, and affiliation history are not covered by the input data and are intentionally omitted.