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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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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
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Grizan, S., Chu, D. C. Y., Wolman, A., & Wattenhofer, R. P. (2015) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/2806777.2806942 · Cited by 10
Combining malleability and I/O control mechanisms to enhance the execution of multiple applications
Singh, D. E., & Carretero, J. (2018) · Journal of Systems and Software · doi:10.1016/j.jss.2018.11.006 · Cited by 10
Fundy: A Scalable and Extensible Resource Manager for Cloud Resources
Ke, X., Guo, C., Ji, S., Bergsma, S., Hu, Z., & Guo, L. (2021) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud53861.2021.00070 · Cited by 10
Improving Energy Efficiency for Mobile Media Cloud via Virtual Machine Consolidation
Dong, Y., Zhou, L., Jin, Y., & Wen, Y. (2015) · Mobile Networks and Applications · doi:10.1007/s11036-015-0595-2 · Cited by 10
Resource efficiency in container-instance clusters
Awada, U., & Barker, A. (2017) · International Conference on Internet of Things, Data and Cloud Computing (ICC) · doi:10.1145/3018896.3056798 · Cited by 10
Co-scheduler: Accelerating Data-Parallel Jobs in Datacenter Networks with Optical Circuit Switching
Li, Z., & Shen, H. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00027 · Cited by 10
Multiple CNN-based Tasks Scheduling across Shared GPU Platform in Research and Development Scenarios
Chen, Z., Luo, L., Quan, W., Shi, Y., Yu, J., Wen, M., & Zhang, C. (2018) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc/smartcity/dss.2018.00107 · Cited by 9
Intelligent Scheduling for Parallel Jobs in Big Data Processing Systems
Xu, M., Wu, C. Q., Hou, A., & Wang, Y. (2019) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2019.8685520 · Cited by 9
Multiple Resource Management and Burst Time Prediction using Deep Reinforcement Learning
Shambharkar, P. G., Bhambri, S., & Kumar, V. (2019) · Computer Science and its Applications · doi:10.15224/978-1-63248-169-6-09 · Cited by 9
Chen, C., Ke, X., Zeyl, T. J., Du, K., Sanjabi, S., Bergsma, S., Pournaghi, R., & Chen, C. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00204 · Cited by 9
Reinforcing Cloud Environments via Index Policy for Bursty Workloads
Balasubramanian, V., Aloqaily, M., Tunde-Onadele, O., Yang, Z., & Martin Reisslein, B. (2020) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms47738.2020.9110417 · Cited by 9
Tenant placement in over-subscribed database-as-a-service clusters
König, A. C., Shan, Y., Ziegler, T., Kakaraparthy, A., Lang, W., Moeller, J., Kalhan, A., & Narasayya, V. R. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3551793.3551814 · Cited by 9
Optimizing MapReduce Framework through Joint Scheduling of Overlapping Phases
Zheng, H., Wan, Z., & Wu, J. (2016) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2016.7568555 · Cited by 9
Marcon, D. S., Mazzola, F., & Barcellos, M. P. (2017) · Computer Networks · doi:10.1016/j.comnet.2017.08.008 · Cited by 9
Job placement using reinforcement learning in GPU virtualization environment
Oh, J., & Kim, Y. (2020) · Cluster Computing · doi:10.1007/s10586-019-03044-7 · Cited by 9
High-Throughput Bin Packing: Scheduling Jobs With Random Resource Demands in Clusters
Psychas, K., & Ghaderi, J. (2020) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2020.3034022 · Cited by 9
Job Scheduling in High Performance ComputingPreprint
Fan, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.09269 · Cited by 9
EMM: Extended matching market based scheduling for big data platform hadoop
Singh, B., & Verma, H. K. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11283-3 · Cited by 8
Cache Coherence Over Disaggregated Memory
Wang, R., Wang, J., & Aref, W. G. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3746405.3746422 · Cited by 8
Multi-Resource List Scheduling of Moldable Parallel Jobs under Precedence Constraints
Perotin, L., Sun, H., & Raghavan, P. (2021) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3472456.3472487 · Cited by 8
Smart-mDAG: An Intelligent Scheduling Method for Multi-DAG Jobs
Zhu, Y., & Hu, B. (2021) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc52510.2021.9621176 · Cited by 8
QoS-Aware Scheduling of Remote Rendering for Interactive Multimedia Applications in Edge Computing
Xie, R., Fang, J., Yao, J., Liu, K., Jia, X., & Wu, K. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3172121 · Cited by 8
Learning Scheduling Policies for Co-Located Workloads in Cloud Datacenters
Li, J., Xiao, D., Yao, J., Long, Y., & Wu, W. (2023) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2023.3319383 · Cited by 8
Leveraging Dependency in Scheduling and Preemption for High Throughput in Data-Parallel Clusters
Liu, J., Shen, H., Sarker, A., & Wingyan Chung, A. (2018) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster.2018.00054 · Cited by 8
Resource scheduling techniques in cloud from a view of coordination: a holistic survey
Wang, Y., Yu, J., & Yu, Z. (2023) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2100298 · Cited by 8
A DRL-based online VM scheduler for cost optimization in cloud brokers
Li, X., Pan, L., & Liu, S. (2023) · World Wide Web · doi:10.1007/s11280-023-01145-3 · Cited by 8
Exploiting Inter-Flow Relationship for Coflow Placement in Datacenters
Huang, X. S., & Ng, T. S. E. (2017) · Asia-Pacific Workshop on Networking (APNet) · doi:10.1145/3106989.3107004 · Cited by 8
Migration-Based Online CPSCN Big Data Analysis in Data Centers
Li, X., Wang, L., Lian, Z., & Qin, X. (2018) · IEEE Access · doi:10.1109/access.2018.2810255 · Cited by 8
Stochastic resource allocation
Funaro, L., Ben-Yehuda, O. A., & Schuster, A. (2019) · ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE) · doi:10.1145/3313808.3313815 · Cited by 8
Comparison of Market-based and DQN methods for Multi-Robot processing Task Allocation (MRpTA)
Gautier, P., Laurent, J., & Diguet, J.-P. (2020) · IEEE International Conference on Robotic Computing (IRC) · doi:10.1109/irc.2020.00060 · Cited by 8
Deep Reinforcement Learning based Energy Scheduling for Edge Computing
Yang, Q., & Li, P. (2020) · IEEE International Conference on Smart Cloud (SmartCloud) · doi:10.1109/smartcloud49737.2020.00041 · Cited by 8
Topology-aware GPU job scheduling with deep reinforcement learning and heuristics
Ayadi, H., An, A., Shao, Y., Pourmedheji, H., Deng, J., Huang, J. X., Feiman, M., & Zhou, H. (2025) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2025.105138 · Cited by 8
Multi-resource Fair Sharing for Multiclass Workflows
Tan, J. R., Zhang, L., Li, M., & Wang, Y. (2015) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/2788402.2788408 · Cited by 7
AdaptiveConfig: Run-Time Configuration of Cluster Schedulers for Cloud Short-Running Jobs
Han, R., Zong, Z., Chen, L. Y., Wang, S., & Zhan, J. (2018) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2018.00158 · Cited by 7
Improving Cluster Resource Efficiency with Oversubscription
Chen, J., Cao, C., Zhang, Y., Ma, X., Zhou, H., & Yang, C. (2018) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2018.00027 · Cited by 7
Jin, Y., Qian, Z., Guo, S., Zhang, S., Wang, X., & Lu, S. (2018) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3225058.3225059 · Cited by 7
A Cost-Efficient and Fair Multi-Resource Allocation Mechanism for Self-Organizing Servers
Khamse-Ashari, J., Lambadaris, I., Kesidis, G., Urgaonkar, B., & Zhao, Y. Q. (2018) · Globecom · doi:10.1109/glocom.2018.8647675 · Cited by 7
Wang, N., Zhou, R., Han, L., Chen, H., & Li, Z. (2022) · IEEE Transactions on Computers · doi:10.1109/tc.2022.3174566 · Cited by 7
Shabka, Z., & Zervas, G. (2022) · Journal of Optical Communications and Networking · doi:10.1364/jocn.478944 · Cited by 6
Cohere: Managing Differential Privacy in Large Scale Systems
Küchler, N., Opel, E., Lycklama, H., Viand, A., & Hithnawi, A. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00122 · Cited by 7
Performance-Aware Scheduling of Parallel Applications on Non-Dedicated Clusters
Cascajo, A., Singh, D. E., & Carretero, J. (2019) · Electronics · doi:10.3390/electronics8090982 · Cited by 7
Low Latency Big Data Processing without Prior Information
Hu, Z., Li, B., Qin, Z., & Goh, R. S. M. (2020) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2019.2910251 · Cited by 7
Reducing Makespans of DAG Scheduling through Interleaving Overlapping Resource Utilization
Duan, Y., Wang, N., & Wang, J. (2020) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass50613.2020.00055 · Cited by 7
Gutierrez-Estevez, D. M., & Luo, M. (2015) · International Telecommunication Networks and Applications Conference (ITNAC) · doi:10.1109/atnac.2015.7366823 · Cited by 7
Mitigating Bottlenecks in Wide Area Data Analytics via Machine Learning
Wang, H., & Li, B. (2018) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2018.2816951 · Cited by 7
QKnober: A Knob-Based Fairness-Efficiency Scheduler for Cloud Computing with QoS Guarantees
Tang, S., Yu, C., Sun, C., Xiao, J. K., & Li, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03596-9_60 · Cited by 7
Xu, G., & Xu, C. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00012 · Cited by 7
Towards Reliable (and Efficient) Job Executions in a Practical Geo-distributed Data Analytics SystemPreprint
Zhang, X., Qian, Z., Zhang, S., Li, Y., Li, X., Wang, X., & Lu, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.00245 · Cited by 7
The Case for Task Sampling based Learning for Cluster Job SchedulingPreprint
Jajoo, A., Hu, Y., Lin, X., & Deng, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.10464 · Cited by 7
Fast and Fair Training for Deep Learning in Heterogeneous GPU Clusters
Mo, Z., Xu, H., & Lau, W. C. (2025) · International Conference on Supercomputing · doi:10.1145/3721145.3728488 · Cited by 8
Sailfish: A Dependency-Aware and Resource Efficient Scheduling for Low Latency in Clouds
Liu, J., Lao, Y., Mao, Y., & Buyya, R. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386947 · Cited by 6
Data-Locality-Aware Task Assignment and Scheduling for Distributed Job Executions
Zhao, H., Tang, X., Chen, P., Yin, J., & Deng, S. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3594158 · Cited by 6
Choudhury, O., Rajan, D., Hazekamp, N., Gesing, S., Thain, D., & Emrich, S. (2015) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster.2015.60 · Cited by 6
Fine-Grained Scheduling in Cloud Gaming on Heterogeneous CPU-GPU Clusters
Zhang, W., Liao, X., Li, P., Jin, H., Lin, L. Z., & Zhou, B. B. (2017) · IEEE Network · doi:10.1109/mnet.2017.1700047 · Cited by 6
Resource-Efficient Index Shard Replication in Large Scale Search Engines
Li, Y., Tang, X., Cai, W., Tong, J., Liu, X., & Wang, G. (2019) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2019.2924423 · Cited by 6
Solver-In-The-Loop Cluster Resource Management for Database-as-a-Service
König, A. C., Shan, Y., Newatia, K., Marshall, L., & Narasayya, V. R. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3625054.3625062 · Cited by 6
DPack: Efficiency-Oriented Privacy Budget Scheduling
Tholoniat, P., Kostopoulou, K., Chowdhury, M., Cidon, A., Geambasu, R., Lécuyer, M., & Yang, J. (2025) · European Conference on Computer Systems · doi:10.1145/3689031.3696096 · Cited by 6
Online packing of arbitrary sized items into designated and multipurpose bins
Goldberg, N., & Karhi, S. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.05.029 · Cited by 6
Optimistic scheduling with service guarantees
Kambatla, K., Yarlagadda, V., Goiri, Í., & Grama, A. Y. (2019) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2019.04.010 · Cited by 6
Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning
Esmaili, A., & Pedram, M. (2020) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed48828.2020.9137025 · Cited by 6
Air-to-Air Collaborative Learning: A Multi-Task Orchestration in Federated Aerial Computing
Awada, U., Zhang, J., Chen, S., & Li, S. (2021) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud53861.2021.00086 · Cited by 6
Towards shorter task completion time in datacenter networks
Zhang, Y., Xu, K., Wang, H., & Shen, M. (2015) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/pccc.2015.7410278 · Cited by 6
Towards Stable Flow Scheduling in Data Centers
Zhang, T., Ren, F., & Shu, R. (2018) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2018.2833458 · Cited by 6
UBIS: Utilization-Aware Cluster Scheduling
Kambatla, K., Yarlagadda, V., Goiri, Í., & Grama, A. Y. (2018) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2018.00045 · Cited by 6
Joint Scheduling of Overlapping MapReduce Phases: Pair Jobs for Optimization
Zheng, H., & Wu, J. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2018.2875698 · Cited by 7
Learning to Optimize DAG Scheduling in Heterogeneous EnvironmentPreprint
Luo, J., Zhou, Y., Xijun, L., Yuan, M., Yao, J., & Zeng, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.06980 · Cited by 6
PPS: Fair and efficient black-box scheduling for multi-tenant GPU clusters
Ma, K., Cai, Z., Yan, X., Zhang, Y., Liu, Z., Feng, Y., Li, C., Lin, W., & Cheng, J. S.-C. (2024) · Parallel Computing · doi:10.1016/j.parco.2024.103082 · Cited by 5
DQN as an alternative to Market-based approaches for Multi-Robot processing Task Allocation (MRpTA)
Gautier, P., & Laurent, J. (2021) · International Journal of Robotic Computing · doi:10.35708/rc1870-126266 · Cited by 5
Deep Reinforcement Agent for Failure-aware Job scheduling in High-Performance Computing
Yang, K., Cao, R., Zhou, Y., Zhang, J., Shao, E., & Tan, G. (2021) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads53394.2021.00061 · Cited by 5
Switch Migration Scheduling in Distributed SDN Controllers
Buckley, M., Zadeh, S. A., Beiruti, M. A., Abbasloo, S., & Ganjali, Y. (2022) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft54395.2022.9844120 · Cited by 5
Resource Management in Cloud Based on Deep Reinforcement Learning
Liu, C., Li, W., Wan, J., Li, L., Ma, Z., & Wang, Y. (2022) · International Conference on Computer Communication and the Internet (ICCCI) · doi:10.1109/iccci55554.2022.9850259 · Cited by 5
MRASS: Dynamic Task Scheduling enabled High Multi-cluster Resource Availability in JointCloud
Gao, F., Wang, H., Shi, P., Fu, X., Zhong, T., & Kong, J. (2022) · IEEE International Conference on Joint Cloud Computing (JCC) · doi:10.1109/jcc56315.2022.00014 · Cited by 5
Shiomoto, K., Otoshi, T., & Murata, M. (2023) · Annals of Telecommunications · doi:10.1007/s12243-022-00940-9 · Cited by 5
Wang, K., Bian, Z., & Chen, Q. (2015) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-css-icess.2015.242 · Cited by 5
Marcon, D. S., Neves, M., Oliveira, R. R., Gaspary, L. P., & Barcellos, M. P. (2016) · Computer Communications · doi:10.1016/j.comcom.2016.07.010 · Cited by 5
Resource Management in Big Data Processing Systems
Tang, S., He, B., Liu, H., & Lee, B.-S. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-805394-2.00007-6 · Cited by 5
A Periodic Task-Oriented Scheduling Architecture in Cloud Computing
Zhang, P., Li, Y., Lin, H., Wang, J., & Zhang, C. (2018) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/bdcloud.2018.00118 · Cited by 5
Matte, V. S., Charapko, A., & Aghayev, A. (2021) · ACM Workshop on Hot Topics in Storage and File Systems (HotStorage) · doi:10.1145/3465332.3470882 · Cited by 5
Deep Reinforcement Learning for Job Scheduling on Cluster
Yao, Z., Chen, L., & Zhang, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86380-7_50 · Cited by 5
Iqbal, S. M., Li, H., Bergsma, S., Beschastnikh, I., & Hu, A. J. (2022) · Symposium on Cloud Computing · doi:10.1145/3542929.3563499 · Cited by 5
PFT: A Performance-Fairness scheduler on Hadoop YARN
Wang, Q., & Huang, X. (2016) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess.2016.7883019 · Cited by 5
A fast solution for bi-objective traffic minimization in geo-distributed data flows
Michailidou, A.-V., & Gounaris, A. (2019) · International Database Engineering and Applications Symposium (IDEAS) · doi:10.1145/3331076.3331107 · Cited by 5
Li, J., Xiao, D., Yang, D., Mo, X., & Wu, W. (2024) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2024.3522333 · Cited by 4
Dai, S., Gao, G., Liu, S., Lim, B.-H., Li, N., Xu, Y., & Zhang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20796-9_1 · Cited by 4
Incentives in Dominant Resource Fair Allocation Under Dynamic Demands
Fikioris, G., Agarwal, R., & Eva Tardos, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71033-9_7 · Cited by 4
The X-flex cross-platform scheduler
Wolf, J. L., Nabi, Z., Nagarajan, V., Saccone, R., Wagle, R., Hildrum, K. W., Pring, E., & Sarpatwar, K. (2014) · doi:10.1145/2676727.2676728 · Cited by 4
A Study of Big Data Computing Platforms: Fairness and Energy Consumption
Niu, Z., & He, B. (2016) · doi:10.1109/ic2ew.2016.31 · Cited by 4
A Virtual Machine Placement Algorithm Combining NSGA-II and Bin-Packing Heuristic
Wei, W., Wang, K., Wang, K., Guo, S., & Gu, H. (2019) · International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) · doi:10.1109/pdcat46702.2019.00044 · Cited by 4
On Max-Min Fairness of Completion Times for Multi-Task Job Scheduling
Shafiee, M., & Ghaderi, J. (2020) · Cited by 4
Probabilistic serial mechanism for multi-type resource allocation
Guo, X., Sikdar, S., Wang, H., Xia, L., Cao, Y., & Wang, H. (2021) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-021-09495-w · Cited by 4
Argus: Efficient Job Scheduling in RDMA-assisted Big Data Processing
Wu, S., Chen, H., Wang, Y., & Jin, H. (2021) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps49936.2021.00092 · Cited by 4
IRLS: An Improved Reinforcement Learning Scheduler for High Performance Computing Systems
Hai, T. H. L., Dinh, L. L., Tien, D. N., Tien, D. B. H., & Thoai, N. (2023) · International Conference on System Science and Engineering · doi:10.1109/icsse58758.2023.10227229 · Cited by 5
CPicker: Leveraging Performance-Equivalent Configurations to Improve Data Center Energy Efficiency
Sun, F. G., Yan, G., He, X. J., Li, H., & Han, Y. (2018) · Journal of Computer Science and Technology · doi:10.1007/s11390-018-1811-x · Cited by 4
Xu, H., Veciana, G. A. D., Lau, W. C., & Zhou, K. (2018) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2018.2871135 · Cited by 4
SPIN: BSP Job Scheduling With Placement-Sensitive Execution
Han, Z., Tan, H., Jiang, S. H.-C., Cao, W., Fu, X., Zhang, L., & Lau, F. C. M. (2021) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2021.3087221 · Cited by 4
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Queueing Theory and Network Applications
Phung-Duc, T., Kasahara, S., & Wittevrongel, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27181-7 · Cited by 4
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Optimizing Internal Overlaps by Self-Adjusting Resource Allocation in Multi-Stage Computing Systems
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Do the Hard Stuff First: Scheduling Dependent Computations in Data-Analytics ClustersPreprint
Grandl, R., Kandula, S., Rao, S., Akella, A., & Kulkarni, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.07371 · Cited by 4
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
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