Apache Hadoop YARN: yet another resource negotiator
Abstract
The initial design of Apache Hadoop [1] was tightly focused on running massive, MapReduce jobs to process a web crawl. For increasingly diverse companies, Hadoop has become the data and computational agorá---the de facto place where data and computational resources are shared and accessed. This broad adoption and ubiquitous usage has stretched the initial design well beyond its intended target, exposing two key shortcomings: 1) tight coupling of a specific programming model with the resource management infrastructure, forcing developers to abuse the MapReduce programming model, and 2) centralized handling of jobs' control flow, which resulted in endless scalability concerns for the scheduler.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
YARN introduces Dominant Resource Fairness for multi-dimensional resource allocation.
“YARN [11] introduces Dominant Resource Fairness for multi-dimensional resource allocation.”
From AgentRM: An OS-Inspired Resource Manager for LLM Agent Systems (2026) · cites this paper as [11]
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References 27 · Cited by 1,848
- Authors
- Vavilapalli, V. K., Murthy, A. C., Douglas, C., Agarwal, S., Konar, M., Evans, R., Graves, T., Lowe, J., Shah, H., Seth, S., Saha, B., Curino, C., O'Malley, O., Radia, S., Reed, B., & Baldeschwieler, E.
- Affiliations
- Hortonworks (United States) (US)
- microsoft.com
- inmobi.com
- yahoo-inc.com
- facebook.com
- Venue
- Proceedings of the 4th annual Symposium on Cloud Computing
- Publisher
- ACM
- Published
- 2013-10
- Pages
- 1-16
- OpenAlex
- W2105947650
- Cited by
- 1,848
- References
- 27
- Citations by year
- 2026: 34 · 2025: 62 · 2024: 58 · 2023: 83 · 2022: 95 · 2021: 155 · 2020: 190 · 2019: 230 · 2018: 229 · 2017: 224 · 2016: 249 · 2015: 155 · 2014: 77 · 2013: 7
- FWCI
- 130.69
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Symposium on Cloud Computing (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
- Hadoop, yarn, resource management, MapReduce, job scheduling, scalability, distributed computing, cluster management
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