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 301–400 of 422
A Data-Driven Approach to Dynamically Adjust Resource Allocation for Compute ClustersPreprint
Pace, F., Milios, D., Damiano Carra, B., Venzano, D., & Pietro Michiardi, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00368 · Cited by 4
Le, T. N., Sun, X., Chowdhury, M., & Liu, Z. (2019) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/3305218.3305246 · Cited by 3
HCoop: A Cooperative and Hybrid Resource Scheduling for Heterogeneous Jobs in Clouds
Liu, J., Gong, R., Dai, W., Zheng, W., Mao, Y., Zhou, W. M., & Deng, F. (2023) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom59040.2023.00046 · Cited by 3
KnapsackLB: Enabling Performance-Aware Layer-4 Load Balancing
Gandhi, R., & Narayana, S. (2025) · Proceedings of the ACM on Networking · doi:10.1145/3709377 · Cited by 3
A Fair and Efficient Resource Allocation Algorithm for Cloud Rendering Jobs
Li, X., Pan, L., Liu, S., & Meng, X. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3570847 · Cited by 3
COBRA: Toward Provably Efficient Semi-Clairvoyant Scheduling in Data Analytics Systems
Zhang, X., Qian, Z., Zhang, S., Li, X., Wang, X., & Lu, S. (2018) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2018.8485981 · Cited by 3
Online Joint Scheduling of Delay-Sensitive and Computation-Oriented Tasks in Edge Computing
Zhang, F., Tang, Z., Lou, J., & Jia, W. (2019) · International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) · doi:10.1109/msn48538.2019.00064 · Cited by 3
Reza, M. F., & Zhao, B. (2021) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas47672.2021.9531852 · Cited by 3
Exact and Approximation Algorithms for PMMS Under Identical Constraints
Dai, S., Gao, G., Guo, X., & Zhang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20350-3_26 · Cited by 3
Semi-Clairvoyant Scheduling in Data Analytics Systems
Zhang, X., Qian, Z., Zhang, S., Li, X., Wang, X., & Lu, S. (2019) · IEEE Transactions on Computers · doi:10.1109/tc.2019.2906193 · Cited by 3
ReLoca: Optimize Resource Allocation for Data-parallel Jobs using Deep Learning
Hu, Z., Li, D., Zhang, D., & Chen, Y. (2020) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom41043.2020.9155521 · Cited by 3
Kaushik, P., Raghavendra, S., Govindaraju, M., & Tiwari, D. (2020) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc48980.2020.00040 · Cited by 3
Scheduling Queues with Simultaneous and Heterogeneous Requirements from Multiple Types of Servers
Zychlinski, N., Chan, C. W., & Dong, J. (2020) · Winter Simulation Conference · doi:10.1109/wsc48552.2020.9383856 · Cited by 3
Extending Hadoop's Yarn Scheduler Load Simulator with a highly realistic network & traffic model
Wette, P., Schwabe, A., Splietker, M., & Karl, H. (2015) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft.2015.7116169 · Cited by 3
MapReduce performance models for Hadoop 2.x
Glushkova, D., Jovanović, P., & Abelló, A. (2017) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · Cited by 3
Xu, H., Veciana, G. A. D., & Lau, W. C. (2017) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2017.8056972 · Cited by 3
FirePlace: Placing Firecraker Virtual Machines with Hindsight Imitation
Balaji, B., Kakovitch, C., & Balakrishnan, N. (2021) · Proceedings of Machine Learning and Systems · Cited by 3
Links as a Service (LaaS): Feeling Alone in the Shared CloudPreprint
Zahavi, E., Shpiner, A., Rottenstreich, O., Avinoam Kolodny, A., & Keslassy, I. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.07395 · Cited by 3
Learning to Schedule DAG TasksPreprint
Hua, Z., Feng, Q., Liu, G., & Yang, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03412 · Cited by 3
Resource Allocation in Disaggregated Data Centre Systems with Reinforcement LearningPreprint
Shabka, Z., & Zervas, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02412 · Cited by 3
Jeon, B., Wang, C., Arroyo, D., Youssef, A., & Gupta, I. (2025) · European Conference on Computer Systems · doi:10.1145/3689031.3696071 · Cited by 2
CAFGD: Reduce Fragmentation in Large-Scale Multi-tenant Clusters for GPU Sharing Workloads
Lao, H., Xu, R., Chen, L., & Zhang, J. (2025) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs63083.2025.00022 · Cited by 2
Bibbens, J., Sigrist, C., Sun, B., Kamali, S., & Hajiesmaili, M. (2026) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3788093 · Cited by 2
Fine-grained scheduling in multi-resource clusters
Zhou, M., Dong, X., Chen, H., & Zhang, X. (2018) · The Journal of Supercomputing · doi:10.1007/s11227-018-2505-4 · Cited by 2
Multi-resource Low-latency Cluster Scheduling without Execution Time Estimation
Yabuuchi, H., & Shinagawa, T. (2020) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid49817.2020.00-62 · Cited by 2
Maximum Nash Social Welfare Under Budget-Feasible EFX
Dai, S., Gao, G., Liu, S., Lim, B.-H., Ning, L., Xu, Y., & Zhang, Y. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3332104 · Cited by 2
Online Non-preemptive Multi-Resource Scheduling for Weighted Completion Time on Multiple Machines
Fan, D., & Liang, B. (2024) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3673038.3673149 · Cited by 2
Fair, Efficient Multi-Resource Scheduling for Stateless Serverless Functions with Anubis
Samanta, A., & Stutsman, R. (2024) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid59990.2024.00021 · Cited by 2
Accelerating the Computation of Multi-Objectives Scheduling Solutions for Cloud Computing
Cérin, C., Menouer, T., & Lebbah, M. (2018) · International Conference on Cloud and Service Computing (CSC) · doi:10.1109/sc2.2018.00014 · Cited by 2
Mira: Sharing Resources for Distributed Analytics at Small Timescales
Kaufmann, M., Kourtis, K., Schüpbach, A., & Zitterbart, M. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622363 · Cited by 2
Online Placing and Pricing for Cloud Container Clusters: An Auction Approach
Zhang, R., & Zhou, R. (2020) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana51271.2020.00019 · Cited by 2
Favoring Eagerness for Remaining Items: Designing Efficient, Fair, and Strategyproof Mechanisms
Guo, X., Sikdar, S., Xia, L., Cao, Y., & Wang, H. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13878 · Cited by 2
Ananthanarayanan, G., & Menache, I. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781316162750.006 · Cited by 2
Self-citationBacklog-Aware SRPT Flow Scheduling in Data Center Networks
Zhang, T., Ren, F., & Shu, R. (2016) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2016.26 · Cited by 2
Priority Scheduling for Heterogeneous Workloads: Tradeoff Between Evictions and Response Time
Çavdar, D., Chen, L. Y., & Alagz, F. (2015) · IEEE Systems Journal · doi:10.1109/jsyst.2015.2455875 · Cited by 2
Resource Management for Parallel Processing Frameworks with Load Awareness at Worker Side
Ha, S.-H., Brown, P., & Pietro Michiardi, A. (2017) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2017.30 · Cited by 2
Joint Optimization of Server and Network Resource Utilization in Cloud Data Centers
Zhou, B., Wu, J., Wang, L., Zhang, F., & Liu, Z. (2017) · Globecom · doi:10.1109/glocom.2017.8254037 · Cited by 2
Li, Z., & Shen, H. (2019) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3337821.3337880 · Cited by 2
Coordinative Scheduling of Computation and Communication in Data-parallel Systems
Li, D., Hu, Z., Lai, Z., Zhang, Y., & Lü, K. (2020) · IEEE Transactions on Computers · doi:10.1109/tc.2020.3039238 · Cited by 2
Evaluation of an Intelligent Task Scheduling Algorithm for 6G 3D Networking
Zhang, J., Wu, H., Shen, S., Bassoli, R., Nguyen, G. T. K., & Fitzek, F. H. P. (2022) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon53508.2022.9842996 · Cited by 2
Multi Resource Scheduling with Task Cloning in Heterogeneous Clusters
Xu, H., Liu, Y., & Lau, W. C. (2022) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3545008.3545093 · Cited by 2
Scaling a Declarative Cluster Manager Architecture with Query Optimization Techniques
Rong, K., Budiu, M., Skiadopoulos, A., Suresh, L., & Tai, A. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3603581.3603599 · Cited by 2
A Complex Task Scheduling Scheme for Big Data Platforms Based on Boolean Satisfiability Problem
Hong, H., Khan, L. R., Ayoade, G., Shaohua, Z., & Wei, Y. X. (2018) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2018.00033 · Cited by 2
MARS: Multi-Scalable Actor-Critic Reinforcement Learning Scheduler
Baheri, B. (2020) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 2
Resource Aware Scheduling for EDA Regression Jobs
Nanda, S., Parthasarathy, G., Choudhary, P., & Venkatachar, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-48340-1_49 · Cited by 2
Xiao, J., Su, W., Li, S., & Liu, H. (2021) · World Wide Web · doi:10.1007/s11280-021-00937-9 · Cited by 2
SAF: Simulated Annealing Fair Scheduling for Hadoop Yarn ClustersPreprint
Ghanavatinasab, M., Bahmani, M., & Azmi, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.12586 · Cited by 2
An Online Planning Agent to Optimize the Policy of Resources Management
Shrivastava, A. D., Thakkar, A., & Chudasama, V. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-0733-2_33 · Cited by 1
DSN: A DDPG-Based Scheduling Framework for Optimal Task Allocation in Cloud Data Centers
Huang, Q., Liu, H., & Zhai, X. (2023) · International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE) · doi:10.1109/cbase60015.2023.10439110 · Cited by 1
Awada, U., Zhang, J., Chen, S., Li, S., & Yang, S. (2024) · Digital Communications and Networks · doi:10.1016/j.dcan.2024.08.002 · Cited by 1
Rethinking and Optimizing Workload Redistribution in Large-scale Internet Data Centers
Zhao, Y., Lv, L., Li, Y., Shen, M., Zhu, L., & Xu, K. (2024) · International Workshop on Quality of Service · doi:10.1109/iwqos61813.2024.10682613 · Cited by 1
Opportunities and Challenges in Service Layer Traffic Engineering
Lim, G., Prerepa, A., Godfrey, P. B., & Mittal, R. (2024) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/3696348.3696871 · Cited by 1
Efficient Far Memory-Aware Scheduling With FaMAS
Lou, C., & Jin, X. (2025) · IEEE Transactions on Networking · doi:10.1109/ton.2025.3574741 · Cited by 1
Reducing Makespan via Optimizing Service Applications Scheduling Without Runtime Estimation
Liu, L., Niu, Z., & Xu, X. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3608216 · Cited by 1
Autonomous AI Agents for Apache Flink Pipeline Management on Kubernetes
Sreedharan, J. (2026) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.4998 · Cited by 1
A Novel Approach to Improving Resource Utilization for IaaS
Dou, H., Qi, Y., & Chen, P. (2015) · Web Information System and Application Conference · doi:10.1109/wisa.2015.46 · Cited by 1
Scheduling Executors with Time-varying Resource Demands on Data-Parallel Computation Frameworks
Hu, Y., & Lin, S. (2017) · DEStech Transactions on Social Science Education and Human Science · doi:10.12783/dtssehs/asshm2016/8355 · Cited by 1
Kilanioti, I., Fernández-Montes, A., Fernández-Cerero, D., Mettouris, C., Nejković, V., Bashroush, R., & Papadopoulos, G. A. (2018) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2018.11.004 · Cited by 1
Scheduling Parallel-Task Jobs Subject to Packing and Placement Constraints
Shafiee, M., & Ghaderi, J. (2022) · Operations Research · doi:10.1287/opre.2021.2198 · Cited by 1
First-Choice Maximality Meets Ex-ante and Ex-post Fairness
Guo, X., Sikdar, S., Xia, L., Cao, Y., & Wang, H. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/303 · Cited by 1
The existence and efficiency of PMMS allocations
Dai, S., Guo, X., Miao, H., Gao, G., Xu, Y., & Zhang, Y. (2024) · Theoretical Computer Science · doi:10.1016/j.tcs.2024.114388 · Cited by 1
Goponenko, A. V., Allan, B. A., Brandt, J. M., & Dechev, D. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/scw63240.2024.00190 · Cited by 1
MR3: An execution framework for hive and spark
Namgung, S., & Park, S. (2025) · ETRI Journal · doi:10.4218/etrij.2024-0488 · Cited by 1
Makespan Minimization for Batch Tasks in Data Centers
Li, X., & Tang, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49145-5_12 · Cited by 1
Packer: Minimizing multi-resource fragmentation and performance interference in datacenters
Marcon, D. S., & Barcellos, M. P. (2017) · IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking.2017.8264836 · Cited by 1
Performance Evaluation of Time Shared and Space Shared Techniques for Cloud Resource Scheduling
Lakkadwala, P., & Kanungo, P. (2019) · International Conference on Data Science and Communication (IconDSC) · doi:10.1109/icondsc.2019.8817025 · Cited by 1
Experimental Demonstration on IT and Network Resources Orchestration in Support of Diverse Computing
Wang, X., Wang, C., Gao, X., Guo, H., & Wu, J. (2019) · Cited by 1
A Cross-Layer Transport Protocol Design in the Terminal Systems of DC
Zhang, Y., & Xu, K. (2020) · Springer eBooks · doi:10.1007/978-981-15-0138-8_4 · Cited by 1
Pocolo: Power Optimized Colocation in Power Constrained Environments
Narayanan, I., Kumar, A., & Sivasubramaniam, A. (2020) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc50251.2020.00010 · Cited by 1
On the Approximability of Related Machine Scheduling Under Arbitrary Precedence
Aggarwal, V., Lan, T., Subramaniam, S., & Xu, M. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3072296 · Cited by 1
Wang, W., Das, S., Wu, X. C., Wang, Z., Chen, A., & Ng, T. S. E. (2021) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/3484266.3487384 · Cited by 1
ThrottleBot - Performance without InsightPreprint
Chang, M. A., Panda, A., Tsai, Y.-C., Wang, H., & Shenker, S. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.00618 · Cited by 1
Decomposition Models for Complex Scheduling Applications
Tran, T. T. (2017) · TSpace (University of Toronto) · Cited by 1
Index Shard Replication Strategies for Improving Resource Utilization in Large Scale Search Engines
Li, Y., Tang, X., Cai, W., Tong, J., Liu, X., Wang, G., Chuansong, G., Cao, X., Geng, G., & Li, M. (2018) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3225058.3225102 · Cited by 1
Automatic Local Rewriting for Combinatorial OptimizationPreprint
Chen, X., & Tian, Y. (2018) · arXiv (Cornell University) · Cited by 1
Terra: Scalable Cross-Layer GDA OptimizationsPreprint
You, J., & Chowdhury, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.08480 · Cited by 1
Nara: Learning Network-Aware Resource Allocation Algorithms for Cloud Data Centres.Preprint
Shabka, Z., & Zervas, G. (2021) · arXiv (Cornell University) · Cited by 1
Aruna, K. B., Kaliraj, V., Sudha, I., & Sureka, V. (2026) · EAI Endorsed Transactions on Internet of Things · doi:10.4108/eetiot.13349
Improved YARN resource scheduling algorithm based on network load sensing
Ding, X., Liu, X., Zhang, R., Tang, S., Sun, L., & Xie, Y. H. (2016) · International Conference on Advanced Electronic Science and Technology (AEST) · doi:10.2991/aest-16.2016.114
Tree Packing for Elastic Machine Allocation in RaaS Cloud
Li, L., Luo, M., & Chou, W. (2016) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2016.0101
Phurti: application and network-aware flow scheduling for MapReduce
Cai, X. (2015) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
HARV: Harnessing hybrid virtualization to improve instance (re)usage in public cloud
Fu, S. D., Zhu, Y., & Liu, J. (2017) · International Workshop on Quality of Service · doi:10.1109/iwqos.2017.7969149
Fair Scheduling in Cloud Datacenters with Multiple Resource Types
Wang, W. (2015) · TSpace (University of Toronto)
Recent Developments in Resource Management in Cloud Computing and Large Computing Clusters
Olaniyan, R., & Maheswaran, M. (2017) · Springer eBooks · doi:10.1007/978-981-10-5026-8_10
Efficient Algorithms for Dynamic Cloud Resource Provisioning
Zhou, R. (2018) · Libraries and Cultural Resources (University of Calgary) · doi:10.11575/prism/31816
Transiency-driven Resource Management for Cloud Computing Platforms
Sharma, P. (2021) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/12471478
Yao, H., Jiang, C., & Qian, Y. (2019) · Wireless networks · doi:10.1007/978-3-030-15028-0_4
Fair and Efficient Resource Management in Heterogeneous Cloud Environments
Khamse-Ashari, J. (2018) · doi:10.22215/etd/2018-13471
Revisiting SRPT for Job Scheduling in Computing Clusters
Xu, H., Wu, H., & Lau, W. C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27181-7_17
Dynamic Resource Shaping for Compute Clusters
Pace, F., Milios, D., Damiano Carra, B., & Pietro Michiardi, A. (2019) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2019.00019
BoPF: Mitigating the Burstiness-Fairness Tradeoff in Multi-Resource ClustersPreprint
Le, T. N., Sun, X., Chowdhury, M., & Liu, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.03523
Reuse-Centric Programming System Support of Machine Learning.
Guan, H. (2020) · NCSU Libraries Repository (North Carolina State University Libraries)
Compressive Sensing based Predictive Online Scheduling with Task Colocation in Cloud Data Center
Chan, Y., Luo, K., & Chen, X. (2020) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads51040.2020.00077
Modelling resource allocation in uncertain system environment through deep reinforcement learningPreprint
Gandhi, N., & Mishra, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09461
Valdez-Vivas, M., Sharma, V., Stanisha, N., Li, S., Mi, L., Jiang, W., Kalinin, A., & Metzler, J. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467119 · Cited by 1
Incentives in Resource Allocation under Dynamic Demands.Preprint
Fikioris, G., Agarwal, R., & Eva Tardos, D. (2021) · arXiv (Cornell University)
Scheduling - A Secret Sauce For Resource Disaggregation
Farshin, A., Roozbeh, A., Schulte, C., Maguire, G. Q., & Kostić, D. (2021) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
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
