The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Survivable Network Design (2011) · Springer eBooks · doi:10.1007/springerreference_57547 · Cited by 3,228
A scalable, commodity data center network architecture
Al-Fares, M., Loukissas, A., & Vahdat, A. M. (2008) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1402946.1402967 · Cited by 3,212
Isard, M., Budiu, M., Yu, Y., Birrell, A. D., & Fetterly, D. (2007) · European Conference on Computer Systems · doi:10.1145/1272996.1273005 · Cited by 2,433
Greenberg, A. G., Hamilton, J. R., Jain, N., Kandula, S., Kim, C., Lahiri, P., Maltz, D. A., Patel, P., & Sengupta, S. (2009) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/1592568.1592576 · Cited by 2,114
Self-citationA scalable, commodity data center network architecture
Al-Fares, M., Loukissas, A., & Vahdat, A. M. (2008) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/1402958.1402967 · Cited by 1,680
Thusoo, A., Sarma, J. S., Jain, N., Shao, Z., Chakka, P., Anthony, S., Liu, H., Wyckoff, P., & Murthy, R. (2009) · Proceedings of the VLDB Endowment · doi:10.14778/1687553.1687609 · Cited by 1,559
Zaharia, M., Borthakur, D., Sarma, J. S., Elmeleegy, K., Shenker, S. J., & Stoica, I. G. (2010) · European Conference on Computer Systems · doi:10.1145/1755913.1755940 · Cited by 1,394
Guo, C., Lu, G., Li, D., Wu, H., Zhang, X., Shi, Y., Tian, C., Zhang, Y., & Lu, S. (2009) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/1592568.1592577 · Cited by 1,374
Dominant resource fairness: fair allocation of multiple resource types
Ghodsi, A., Zaharia, M., Hindman, B., Konwinski, A., Shenker, S. J., & Stoica, I. G. (2011) · doi:10.5555/1972457.1972490 · Cited by 1,120
Vazirani, V. V. (2003) · Springer eBooks · doi:10.1007/978-3-662-04565-7 · Cited by 930
Flagged first, then by citations
Cited by
Sorted by citations
Items 401–422 of 422
Doing More by Doing Less: How Structured Partial Backpropagation Improves Deep Learning ClustersPreprint
Kumar, A., Subramanian, K., Venkataraman, S., & Akella, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.10672
Self-citationA CVD Critical Level-aware Scheduling Model Based on Reinforcement Learning for ECG Service Request
Gao, J., Yu, J., Wang, N., Feng, P., Cheng, H., Zhou, B., & Wang, Z. (2022) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc55534.2022.00039
Maximin-Aware Allocations of Indivisible Chores with Symmetric and Asymmetric Agents
Wei, T., Li, B., & Li, M. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/323
Mr3: An Execution Framework for Hive and SparkPreprint
Park, S., Namgung, S., & Kang, B. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4587845
Sagisaka, T., Shiomoto, K., & Kurimoto, T. (2023) · International Conference on the Network of the Future (NOF) · doi:10.1109/nof58724.2023.10302803
Deep Reinforcement Learning Framework with Q Learning For Optimal Scheduling in Cloud Computing
Sabat, E. A. N. R. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i10.8464
Pyxis: Scheduling Mixed Tasks in Disaggregated Datacenters
Qi, S., Jin, C., Chowdhury, M., Liu, Z., Liu, X., & Jin, X. (2024) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2024.3418620
Nona: A Stochastic Congestion-Aware Job Scheduler for Real-Time Inference Queries
Pit-Claudel, B., Malak, D., Cohen, A., Médard, M., & Ghobadi, M. (2024) · IEEE International Conference on Cloud Networking · doi:10.1109/cloudnet62863.2024.10815926
Allocating Public Goods via Dynamic Max-Min Fairness: Long-Run Behavior and Competitive Equilibria
Onyeze, C., Banerjee, S., Fikioris, G., & Eva Tardos, D. (2025) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3711695
Deep reinforcement learning-based multi-objective job scheduling scheme for load balancing
Tao, Y., & Liu, J. (2026) · Cluster Computing · doi:10.1007/s10586-026-05979-0
Fitting the Void: Residual-Aware Geometric Packing for GenAI Workloads
Kaarat, A., Batthula, V. J. R., & Segall, R. S. (2026) · International Informatics and Software Engineering Conference (IISEC) · doi:10.1109/iisec69317.2026.11418430
Contribution des méthodes d'apprentissage à la distribution de tâches dans un cluster robotique
Gautier, P. (2021) · theses.fr (ABES) · doi:10.70675/91022df3z18c7z4caazb369za24bb3ad4094
Learning to Process Relational Information for Cloud Computing Task Scheduling
Hong, S.-H., Kim, M., & Lee, B.-J. (2026) · Machine Learning · doi:10.1007/s10994-026-07043-9
Cross-sector management for workload scheduling and thermal environment in data centers
Guo, H., Yu, H., & Nik, V. M. (2026) · Energy · doi:10.1016/j.energy.2026.141508
Zheng, B., Yuan, Y., Tang, H. A. N. Z. E., Luo, W., & Yi, T. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7171304
Incentives and Evidence in Learned Service Orchestration
Khilji, S. I., Furutanpey, A., & Dustdar, S. (2026) · International Symposium on Service Oriented Software Engineering · doi:10.1109/sose71128.2026.00015
Awine, S. J., & Liu, J. (2026) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud72782.2026.00027
Real-Time Scheduling Based on Service Dependency Graphs for Resource-Aware Service Orchestration
Wimalagunasekara, D., Gamage, Y., Sarathchandra, T., Samarathunge, P., Jayasinghearachchi, V., & Kasthurirathna, D. (2026) · International Conference on Computer Communication and the Internet (ICCCI) · doi:10.1109/iccci70321.2026.11666506
Janus: Multi-LLM Serving at Production Scale
Zhou, T., Wang, Y., Zhou, Y., Liu, Z., Wang, Z., Peng, Y., Wang, Y., Zhang, Y., Yin, J., Tian, K., et al. (2026) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3830418.3843867
EASE: Resource-aware Query Scheduling across Heterogeneous Cloud Compute Services
Li, W., Bian, H., Zhang, C., & Li, G. (2026) · Proceedings of the ACM on Management of Data · doi:10.1145/3837108
HAIL: Prediction-Calibrated Hierarchical Resource Isolation for Multi-Tenant Cloud Platforms
Kapoor, L., Huang, L., & Arora, S. (2026) · Journal of Computing and Electronic Information Management · doi:10.54097/2jvszb05
(2017) · Networked Systems Design and Implementation · Cited by 329
Self-citation
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
