The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Large-scale cluster management at Google with Borg
Open accessAbstract
Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from many thousands of different applications, across a number of clusters each with up to tens of thousands of machines.
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“Borg [25], the Kubernetes lineage [52], Tetris [26], Hawk [27], and Sparrow [28] pack stateless jobs; Gaia [22] optimizes geo-distributed training .”
From Janus: Joint Prefill/Decode Disaggregation with KV-Cache-Aware Multi-Cloud Routing for Edge-Adjacent LLM Serving (2026) · cites this paper as [25]
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In the citing paper
“Google's Borg [27] and Kubernetes [28] handle container orchestration with resource limits and quotas.”
From AgentRM: An OS-Inspired Resource Manager for LLM Agent Systems (2026) · cites this paper as [27]
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Citations
Cites
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Dean, J., & Barroso, L. A. (2013) · Communications of the ACM · doi:10.1145/2408776.2408794 · Cited by 1,852
Apache Hadoop YARN: yet another resource negotiator
Vavilapalli, V. K., Murthy, A. C., Douglas, C., Agarwal, S., Konar, M., Evans, R., Graves, T., Lowe, J., Shah, H., Seth, S., et al. (2013) · Proceedings of the 4th annual Symposium on Cloud Computing · doi:10.1145/2523616.2523633 · Cited by 1,848
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Cited by
Sorted by citations
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
“In our distributed environment, these different tasks are containers in jobs managed by a cluster scheduling sys-tem [51].”
Network Function Virtualization: State-of-the-Art and Research Challenges
Mijumbi, R., Serrat, J., Gorricho, J.-L., Bouten, N., De Turck, F., & Boutaba, R. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2477041 · Cited by 1,933
“In addition, existing scheduling tools such as Google’s Borg [174] and Apache Mesos [175] may be considered for scheduling of VNFs. Borg uses task-packing, over-commitment, and machine sharing with process-level performance isolation to run multiple jobs, from many applications, across a number of clusters.”
MethodLearning scheduling algorithms for data processing clusters
Mao, H., Schwarzkopf, M., Venkatakrishnan, S. B., Meng, Z., & Alizadeh, M. (2019) · ACM Special Interest Group on Data Communication · doi:10.1145/3341302.3342080 · Cited by 831
“A good scheduling policy packs work tightly to reduce fragmentation [18; 19; 46], prioritizes jobs according to high-level metrics such as user-perceived latency [47], and avoids inefficiencies due to incorrect job configurations [14].”
BackgroundInfluentialBurns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016) · Communications of the ACM · doi:10.1145/2890784 · Cited by 690
Self-citation
Metadata
- Authors
- Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., & Wilkes, J.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the Tenth European Conference on Computer Systems
- Publisher
- ACM
- Published
- 2015-04-17
- Pages
- 1-17
- OpenAlex
- W2141992894
- Semantic Scholar
- 3a043714354fe498752b45e4cf429dbae0fb2558
- Cited by
- 1,468
- References
- 63
- Citations by year
- 2026: 154 · 2025: 139 · 2024: 110 · 2023: 115 · 2022: 114 · 2021: 156 · 2020: 129 · 2019: 152 · 2018: 141 · 2017: 114 · 2016: 119 · 2015: 25
- FWCI
- 115.57
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- European Conference on Computer Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed and Parallel Computing Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Keywords
- cluster management, job scheduling, large-scale distributed systems, Google, cluster computing, resource management
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