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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“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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Sorted by citations
TensorFlow: A system for large-scale machine learningPreprint
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“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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