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Multi-resource packing for cluster schedulers
Open accessAbstract
Tasks in modern data parallel clusters have highly diverse resource requirements, along CPU, memory, disk and network. Any of these resources may become bottlenecks and hence, the likelihood of wasting resources due to fragmentation is now larger. Today's schedulers do not explicitly reduce fragmentation. Worse, since they only allocate cores and memory, the resources that they ignore (disk and network) can be over-allocated leading to interference, failures and hogging of cores or memory that could have been used by other tasks. We present Tetris, a cluster scheduler that packs, i.e., matches multi-resource task requirements with resource availabilities of machines so as to increase cluster efficiency (makespan). Further, Tetris uses an analog of shortest-running-time-first to trade-off cluster efficiency for speeding up individual jobs. Tetris' packing heuristics seamlessly work alongside a large class of fairness policies. Trace-driven simulations and deployment of our prototype on a 250 node cluster shows median gains of 30% in job completion time while achieving nearly perfect fairness.
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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 [26]
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Metadata
- Authors
- Grandl, R., Ananthanarayanan, G., Kandula, S., Rao, S., & Akella, A.
- Affiliations
- Microsoft (United States) (US)
- Venue
- Proceedings of the 2014 ACM conference on SIGCOMM
- Publisher
- ACM
- Published
- 2014-08-17
- Pages
- 455-466
- OpenAlex
- W2133569115
- Cited by
- 421
- References
- 29
- Citations by year
- 2026: 13 · 2025: 13 · 2024: 19 · 2023: 28 · 2022: 25 · 2021: 54 · 2020: 44 · 2019: 59 · 2018: 56 · 2017: 42 · 2016: 32 · 2015: 34 · 2014: 2
- FWCI
- 28.55
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGCOMM Conference (SIGCOMM) (publishedVersion)
- Funders
- National Science Foundation (1302041, 1139158, 1040757)
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed and Parallel Computing Systems
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Keywords
- cluster scheduling, resource fragmentation, makespan, job completion time
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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