---
title: "google/cluster-data: papers citing this GitHub repository"
description: "7 papers in the tracked journals link to the GitHub repository google/cluster-data in their Crossref records, the first in 2020; most of them (2) in 2025."
url: https://scholar4ai.com/code/github-google-cluster-data
markdown_url: https://scholar4ai.com/code/github-google-cluster-data.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "7 papers in the tracked journals link to the GitHub repository google/cluster-data in their Crossref records, the first in 2020; most of them (2) in 2025. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# google/cluster-data: papers linking to it

Research papers in the 480 tracked journals that link to google/cluster-data (https://github.com/google/cluster-data), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- GitHub
- 1,112 stars
- 217 forks
- TeX
- created 2015
- https://github.com/google/cluster-data

All repositories: https://scholar4ai.com/code.md

## Key findings

- 7 papers in the tracked journals link to the GitHub repository google/cluster-data in their Crossref records, the first in 2020; most of them (2) in 2025.
- By category: AI (5), Information Systems (1) and Operations Research (1); the journals with the most are Neural Computing and Applications (2), Applied Intelligence (1) and Journal of Industrial Information Integration (1).

## By year

*Papers linking to google/cluster-data by publication year*

| Year | Papers |
|---|---:|
| 2020 | 1 |
| 2021 | 1 |
| 2022 | 1 |
| 2024 | 1 |
| 2025 | 2 |
| 2026 | 1 |

## Journals

*Journals whose papers link to google/cluster-data most*

| Journal | Papers |
|---|---:|
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |
| [Journal of Industrial Information Integration](https://scholar4ai.com/journals/journal-of-industrial-information-integration.md) | 1 |
| [Queueing Systems](https://scholar4ai.com/journals/queueing-systems.md) | 1 |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 1 |

## Papers

*Papers linking to google/cluster-data, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-01-27 | [Open problems in queueing theory inspired by datacenter computing](https://doi.org/10.1007/s11134-020-09684-6) | [Queueing Systems](https://scholar4ai.com/journals/queueing-systems.md) | Mor Harchol‐Balter | 62 | cites it |
| 2022-02-09 | [Workflow performance prediction based on graph structure aware deep attention neural network](https://doi.org/10.1016/j.jii.2022.100337) | [Journal of Industrial Information Integration](https://scholar4ai.com/journals/journal-of-industrial-information-integration.md) | Jixiang Yu, Ming Yu Gao, Yuchan Li et al. | 35 | cites it |
| 2020-01-29 | [Learning-based power prediction for geo-distributed Data Centers: weather parameter analysis](https://doi.org/10.1186/s40537-020-0284-2) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | Somayyeh Taheri, Maziar Goudarzi, Osamu Yoshie | 24 | cites it |
| 2025-12-05 | [Federated LSTM autoencoders for time series anomaly detection in production-scale HPC systems](https://doi.org/10.1016/j.knosys.2025.115043) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Emmen Farooq, Michela Milano, Andrea Borghesi | 5 | cites it |
| 2024-09-18 | [DAGCN: hybrid model for efficiently handling joint node and link prediction in cloud workflows](https://doi.org/10.1007/s10489-024-05828-w) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Ruimin Ma, Junqi Gao, Li Cheng et al. | 2 | cites it |
| 2025-10-15 | [CloudSched-GA: an adaptive genetic optimizer for efficient and balanced task scheduling in cloud ecosystems](https://doi.org/10.1007/s00521-025-11668-7) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Fatma M. Talaat, Alyaa A. Hamza | 1 | cites it |
| 2026-06-01 | [An intelligent scheduling and resource prediction framework using multidimensional data streams and deep reinforcement learning](https://doi.org/10.1007/s00521-026-11917-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Wei Deng, Ziliang Qiu, X F Liu et al. | 0 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). google/cluster-data: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-google-cluster-data

```bibtex
@misc{scholar4ai-code-github-google-cluster-data,
  author = {{Scholar4AI}},
  title = {google/cluster-data: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-google-cluster-data}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).