Multi-resource packing for cluster schedulers

Approximation Algorithms

Survivable Network Design (2011) · SpringerReference · doi:10.1007/springerreference_57547 · Cited by 3,258 · see citing works

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 · see citing works

Dryad

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 · see citing works

VL2

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 · see citing works

A 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 · see citing works

Hive

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 · see citing works

Delay scheduling

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 · see citing works

BCube

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 · see citing works

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 · see citing works

Quincy

Isard, M., Prabhakaran, V., Currey, J., Wieder, U., Talwar, K., & Goldberg, A. V. (2009) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/1629575.1629601 · Cited by 856 · see citing works

Approximation Algorithms

Vazirani, V. V. (2003) · Springer eBooks · doi:10.1007/978-3-662-04565-7 · Cited by 720 · see citing works

Managing data transfers in computer clusters with orchestra

Chowdhury, M., Zaharia, M., Ma, J. H., Jordan, M. I., & Stoica, I. G. (2011) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/2018436.2018448 · Cited by 547 · see citing works

Beowulf Cluster Computing with Linux

(2001) · The MIT Press eBooks · doi:10.7551/mitpress/1556.001.0001 · Cited by 383 · see citing works

Condor: A Distributed Job Scheduler

Tannenbaum, T., Wright, D., Miller, K., & Livny, M. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/1556.003.0019 · Cited by 380 · see citing works

Sharing the data center network

Shieh, A., Kandula, S., Greenberg, A. G., Kim, C., & Saha, B. (2011) · doi:10.5555/1972457.1972489 · Cited by 342 · see citing works

Approximation Algorithms

Körte, B., & Vygen, J. (2002) · Algorithms and combinatorics · doi:10.1007/978-3-662-21711-5_16 · Cited by 287 · see citing works

SPEED

Gulwani, S., Mehra, K. K., & Chilimbi, T. M. (2009) · ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL) · doi:10.1145/1480881.1480898 · Cited by 266 · see citing works

Heuristics for Vector Bin Packing

Panigrahy‎, R., Talwar, K., Uyeda, L., & Wieder, U. (2011) · Cited by 203 · see citing works

Connection Scheduling in Web Servers

Crovella, M. E., Frangioso, R., & Harchol‐Balter, M. (1999) · Cited by 200 · see citing works

Re-optimizing data-parallel computing

Agarwal, S., Kandula, S., Bruno, N., Wu, M.-C., Stoica, I. G., & Zhou, J. (2012) · Cited by 174 · see citing works

Classifying scheduling policies with respect to unfairness in an M/GI/1

Wierman, A., & Harchol‐Balter, M. (2003) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/781027.781057 · Cited by 170 · see citing works

SCOPE: parallel databases meet MapReduce

Zhou, J., Bruno, N., Wu, M.-C., Larson, P.-Å., Chaiken, R., & Shakib, D. (2012) · The VLDB Journal · doi:10.1007/s00778-012-0280-z · Cited by 152 · see citing works

Leveraging endpoint flexibility in data-intensive clusters

Chowdhury, M., Kandula, S., & Stoica, I. G. (2013) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/2486001.2486021 · Cited by 150 · see citing works

There is no asymptotic PTAS for two-dimensional vector packing

Woeginger, G. J. (1997) · Information Processing Letters · doi:10.1016/s0020-0190(97)00179-8 · Cited by 124 · see citing works

Themis

Rasmussen, A., Lam, V. T., Conley, M., Porter, G., Kapoor, R., & Vahdat, A. M. (2012) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/2391229.2391242 · Cited by 72 · see citing works

Tight bounds for online vector bin packing

Azar, Y., Cohen, I. R., Kamara, S., & Shepherd, F. B. (2013) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/2488608.2488730 · Cited by 59 · see citing works

Predictive VM consolidation on multiple resources: Beyond load balancing

Lu, L., Zhang, H., Evgenia Smirni, A., Jiang, G., & Yoshihira, K. (2013) · International Workshop on Quality of Service · doi:10.1109/iwqos.2013.6550268 · Cited by 20 · see citing works

Tail asymptotics for policies favoring short jobs in a many-flows regime

Yang, C.-W., Wierman, A., Shakkottai, S., & Harchol‐Balter, M. (2006) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/1140103.1140290 · Cited by 15 · see citing works

Tail asymptotics for policies favoring short jobs in a many-flows regime

Yang, C.-W., Wierman, A., Shakkottai, S., & Harchol‐Balter, M. (2006) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/1140277.1140290 · Cited by 8 · see citing works

Large-scale cluster management at Google with Borg

Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., & Wilkes, J. (2015) · Proceedings of the Tenth European Conference on Computer Systems · doi:10.1145/2741948.2741964 · Cited by 1,468 · see citing works

Resource Management with Deep Reinforcement Learning

Mao, H., Alizadeh, M., Menache, I., & Kandula, S. (2016) · Proceedings of the 15th ACM Workshop on Hot Topics in Networks · doi:10.1145/3005745.3005750 · Cited by 1,353 · see citing works

Learning scheduling algorithms for data processing clusters

Mao, H., Schwarzkopf, M., Venkatakrishnan, S. B., Meng, Z., & Alizadeh, M. (2018) · Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication · doi:10.1145/3341302.3342080 · Cited by 899 · see citing works

Optimus: an efficient dynamic resource scheduler for deep learning clusters

Peng, Y.-H., Bao, Y., Chen, Y.-R., Wu, C., & Guo, C.-X. (2018) · European Conference on Computer Systems · doi:10.1145/3190508.3190517 · Cited by 544 · see citing works

Pond: CXL-Based Memory Pooling Systems for Cloud Platforms

Li, H., Berger, D. S., Hsu, L., Ernst, D., Zardoshti, P., Novaković, S., Shah, M., Rajadnya, S., Lee, S., Agarwal, I., et al. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3575693.3578835 · Cited by 352 · see citing works

Autopilot

Rzaḑca, K., Findeisen, P., Swiderski, J., Zych, P., Broniek, P., Kusmierek, J., Nowak, P. K., Strack, B., Witusowski, P., Hand, S., et al. (2020) · European Conference on Computer Systems · doi:10.1145/3342195.3387524 · Cited by 306 · see citing works

Low Latency Geo-distributed Data Analytics

Pu, Q., Ananthanarayanan, G., Bodik, P., Kandula, S., Akella, A., Bahl, P., & Stoica, I. (2015) · Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication · doi:10.1145/2785956.2787505 · Cited by 246 · see citing works

Using GANs for Sharing Networked Time Series Data

Lin, Z., Jain, A., Wang, C., Fanti, G. C., & Sekar, V. (2020) · ACM Internet Measurement Conference (IMC) · doi:10.1145/3419394.3423643 · Cited by 232 · see citing works

Efficient memory disaggregation with INFINISWAP

Gu, J., Lee, Y., Zhang, Y., Chowdhury, M., & Shin, K. G. (2017) · Networked Systems Design and Implementation · Cited by 209 · see citing works

Machine Learning Methods for Reliable Resource Provisioning in Edge-Cloud Computing

Duc, T. L., Leiva, R. A. G., Casari, P., & Östberg, P.-O. (2019) · ACM Computing Surveys · doi:10.1145/3341145 · Cited by 207 · see citing works

Learning to Perform Local Rewriting for Combinatorial OptimizationPreprint

Chen, X., & Tian, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.00337 · Cited by 202 · see citing works

Scalable Deep Learning on Distributed Infrastructures

Mayer, R., & Jacobsen, H. (2019) · ACM Computing Surveys · doi:10.1145/3363554 · Cited by 234 · see citing works

Morpheus: towards automated SLOs for enterprise clusters

Jyothi, S. A., Curino, C., Menache, I., Narayanamurthy, S., Tumanov, A., Yaniv, J., Mavlyutov, R., Goiri, Í., Krishnan, S., Kulkarni, J., et al. (2016) · Operating Systems Design and Implementation · doi:10.5555/3026877.3026887 · Cited by 179 · see citing works

Datacenter Traffic Control: Understanding Techniques and Tradeoffs

Noormohammadpour, M., & Raghavendra, C. S. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2782753 · Cited by 177 · see citing works

Low Latency Geo-distributed Data Analytics

Pu, Q., Ananthanarayanan, G., Bodík, P., Kandula, S., Akella, A., Bahl, P., & Stoica, I. G. (2015) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2829988.2787505 · Cited by 177 · see citing works

Can far memory improve job throughput?

Amaro, E., Branner-Augmon, C., Luo, Z., Ousterhout, A., Aguilera, M. K., Panda, A., Ratnasamy, S., & Shenker, S. J. (2020) · European Conference on Computer Systems · doi:10.1145/3342195.3387522 · Cited by 168 · see citing works

NetLLM: adapting large language models for networking

Wu, D., Wang, X., Qiao, Y., Wang, Z., Jiang, J., Cui, S., & Wang, F. (2024) · Proceedings of the ACM SIGCOMM 2024 Conference · doi:10.1145/3651890.3672268 · Cited by 167 · see citing works

Dedas: Online Task Dispatching and Scheduling with Bandwidth Constraint in Edge Computing

Meng, J., Tan, H., Xu, C., Cao, W., Liu, L., & Li, B. (2019) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2019.8737577 · Cited by 159 · see citing works

Network-Aware Scheduling for Data-Parallel Jobs

Jalaparti, V., Bodík, P., Menache, I., Rao, S., Makarychev, K., & Caesar, M. (2015) · ACM Special Interest Group on Data Communication · doi:10.1145/2785956.2787488 · Cited by 157 · see citing works

Multi-Resource Fair Allocation in Heterogeneous Cloud Computing Systems

Wang, W., Liang, B., & Li, B. (2014) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2014.2362139 · Cited by 156 · see citing works

Graphene: packing and dependency-aware scheduling for data-parallel clusters

Grandl, R., Kandula, S., Rao, S., Akella, A., & Kulkarni, J. (2016) · Operating Systems Design and Implementation · doi:10.5555/3026877.3026885 · Cited by 155 · see citing works

Adaptive and Efficient Resource Allocation in Cloud Datacenters Using Actor-Critic Deep Reinforcement Learning

Chen, Z., Hu, J. W., Min, G., Luo, C., & El-Ghazawi, T. A. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3132422 · Cited by 154 · see citing works

Deep Learning-based Job Placement in Distributed Machine Learning Clusters

Bao, Y., Peng, Y., & Wu, C. (2019) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2019.8737460 · Cited by 148 · see citing works

Altruistic scheduling in multi-resource clusters

Grandl, R., Chowdhury, M., Akella, A., & Ananthanarayanan, G. (2016) · Operating Systems Design and Implementation · doi:10.5555/3026877.3026884 · Cited by 139 · see citing works

Leveraging Deep Reinforcement Learning for Traffic Engineering: A Survey

Xiao, Y., Liu, J., Wu, J., & Ansari, N. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3102580 · Cited by 137 · see citing works

Mercury: hybrid centralized and distributed scheduling in large shared clusters

Karanasos, K., Rao, S., Curino, C., Douglas, C., Chaliparambil, K., Fumarola, G. M., Heddaya, S., Ramakrishnan, R., & Sakalanaga, S. (2015) · USENIX Annual Technical Conference · Cited by 130 · see citing works

Cloud Resource Scheduling With Deep Reinforcement Learning and Imitation Learning

Guo, W., Tian, W., Ye, Y., Xu, L., & Wu, K. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3025015 · Cited by 122 · see citing works

Cloud-Native Computing: A Survey From the Perspective of Services

Deng, S., Zhao, H., Huang, B., Zhang, C., Chen, F., Deng, Y., Yin, J., Dustdar, S., & Zomaya, A. Y. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3353855 · Cited by 119 · see citing works

HUG: multi-resource fairness for correlated and elastic demands

Chowdhury, M., Liu, Z., Ghodsi, A., & Stoica, I. G. (2016) · Networked Systems Design and Implementation · Cited by 116 · see citing works

Hopper

Ren, X., Ananthanarayanan, G., Wierman, A., & Yu, M. (2015) · ACM Special Interest Group on Data Communication · doi:10.1145/2785956.2787481 · Cited by 114 · see citing works

DL2: A Deep Learning-Driven Scheduler for Deep Learning Clusters

Peng, Y., Bao, Y., Chen, Y., Wu, C., Meng, C., & Lin, W. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3052895 · Cited by 112 · see citing works

Clustering With Outlier Removal

Liu, H., Li, J., Wu, Y., & Fu, Y. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2954317 · Cited by 106 · see citing works

OnDisc: Online Latency-Sensitive Job Dispatching and Scheduling in Heterogeneous Edge-Clouds

Han, Z., Tan, H., Li, X., Jiang, S. H.-C., Li, Y., & Lau, F. C. M. (2019) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2019.2953806 · Cited by 100 · see citing works

Efficient queue management for cluster scheduling

Rasley, J., Karanasos, K., Kandula, S., Fonseca, R., Vojnović, M., & Rao, S. (2016) · European Conference on Computer Systems · doi:10.1145/2901318.2901354 · Cited by 96 · see citing works

CLARINET: WAN-aware optimization for analytics queries

Viswanathan, R., Ananthanarayanan, G., & Akella, A. (2016) · Minds at UW (University of Wisconsin) · doi:10.5555/3026877.3026911 · Cited by 95 · see citing works

MIRAS: Model-based Reinforcement Learning for Microservice Resource Allocation over Scientific Workflows

Yang, Z., Nguyen, P., Jin, H., & Nahrstedt, K. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00021 · Cited by 94 · see citing works

Ekya: Continuous Learning of Video Analytics Models on Edge Compute ServersPreprint

Bhardwaj, R., Xia, Z., Ananthanarayanan, G., Jiang, J., Karianakis, N., Shu, Y., Hsieh, K., Bahl, V., & Stoica, I. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.10557 · Cited by 95 · see citing works

Themis: Fair and Efficient GPU Cluster SchedulingPreprint

Mahajan, K., Balasubramanian, A., Singhvi, A., Venkataraman, S., Akella, A., Phanishayee, A., & Chawla, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.01484 · Cited by 86 · see citing works

Optimal Transcoding and Caching for Adaptive Streaming in Media Cloud: an Analytical Approach

Jin, Y., Wen, Y., & Westphal, C. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2402892 · Cited by 85 · see citing works

DeepJS

Li, F., & Hu, B. (2019) · International Conference on Big Data and Computing (ICBDC) · doi:10.1145/3335484.3335513 · Cited by 83 · see citing works

AlloX

Le, T. N., Sun, X., Chowdhury, M., & Liu, Z. (2020) · European Conference on Computer Systems · doi:10.1145/3342195.3387547 · Cited by 82 · see citing works

Multi-resource interleaving for deep learning training

Zhao, Y., Liu, Y., Peng, Y., Zhu, Y., Liu, X., & Jin, X. (2022) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3544216.3544224 · Cited by 82 · see citing works

Multi-resource scheduling and power simulation for cloud computing

Lin, W., Xu, S., He, L., & Li, J. (2017) · Information Sciences · doi:10.1016/j.ins.2017.02.054 · Cited by 79 · see citing works

Stratus

Chung, A., Park, J. W., & Ganger, G. R. (2018) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3267809.3267819 · Cited by 78 · see citing works

Online parameter optimization for elastic data stream processing

Heinze, T., Roediger, L., Meister, A., Ji, Y., Jerzak, Z., & Fetzer, C. W. (2015) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/2806777.2806847 · Cited by 74 · see citing works

Spear: Optimized Dependency-Aware Task Scheduling with Deep Reinforcement Learning

Hu, Z., Tu, J., & Li, B. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00201 · Cited by 73 · see citing works

Monotasks

Ousterhout, K., Canel, C., Ratnasamy, S., & Shenker, S. J. (2017) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3132747.3132766 · Cited by 72 · see citing works

History-based harvesting of spare cycles and storage in large-scale datacenters

Zhang, Y., Prekas, G., Fumarola, G. M., da Fontoura, M. F. M. C., Goiri, Í., & Bianchini, R. (2016) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5555/3026877.3026935 · Cited by 70 · see citing works

Wide-area analytics with multiple resources

Hung, C.-C., Ananthanarayanan, G., Golubchik, L., Yu, M., & Zhang, M. (2018) · European Conference on Computer Systems · doi:10.1145/3190508.3190528 · Cited by 69 · see citing works

CloudBuild

Esfahani, H., Fietz, J., Ke, Q., Kolomiets, A., Lan, E., Mavrinac, E., Schulte, W., Sanches, N., & Kandula, S. (2016) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/2889160.2889222 · Cited by 68 · see citing works

Crux: GPU-Efficient Communication Scheduling for Deep Learning Training

Cao, J., Guan, Y., Qian, K., Gao, J., Xiao, W., Dong, J., Fu, B., Cai, D., & Zhai, E. (2024) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3651890.3672239 · Cited by 67 · see citing works

Characterizing and Synthesizing Task Dependencies of Data-Parallel Jobs in Alibaba Cloud

Tian, H., Zheng, Y., & Wang, W. (2019) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3357223.3362710 · Cited by 66 · see citing works

Deep Reinforcement Agent for Scheduling in HPC

Fan, Y., Lan, Z., Childers, J. T., Rich, P., Allcock, W. E., & Papka, M. E. (2021) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps49936.2021.00090 · Cited by 64 · see citing works

3Sigma

Park, J. W., Tumanov, A., Jiang, A. H., Kozuch, M. A., & Ganger, G. R. (2018) · European Conference on Computer Systems · doi:10.1145/3190508.3190515 · Cited by 63 · see citing works

Mapreduce performance model for Hadoop 2.x

Glushkova, D., Jovanović, P., & Abelló, A. (2017) · Information Systems · doi:10.1016/j.is.2017.11.006 · Cited by 59 · see citing works

A Survey of the State-of-the-Art in Fair Multi-Resource Allocations for Data Centers

Poullie, P. G., Bocek, T., & Stiller, B. (2017) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2017.2743066 · Cited by 59 · see citing works

Cluster resource scheduling in cloud computing: literature review and research challenges

Khallouli, W., & Huang, J. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-04138-z · Cited by 58 · see citing works

An Efficient and Fair Multi-Resource Allocation Mechanism for Heterogeneous Servers

Khamse-Ashari, J., Lambadaris, I., Kesidis, G., Urgaonkar, B., & Zhao, Y. Q. (2018) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2018.2841915 · Cited by 56 · see citing works

Owl

Tian, H., Li, S., Wang, A., Wang, W., Wu, T., & Yang, H. (2022) · Symposium on Cloud Computing · doi:10.1145/3542929.3563470 · Cited by 55 · see citing works

RLSK: A Job Scheduler for Federated Kubernetes Clusters based on Reinforcement Learning

Huang, J., Xiao, C., & Wu, W. (2020) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e48712.2020.00019 · Cited by 55 · see citing works

Towards cost-effective and elastic cloud database deployment via memory disaggregation

Zhang, Y., Ruan, C., Li, C., Yang, X., Cao, W., Li, F., Wang, B., Fang, J. Z., Wang, Y., Huo, J., et al. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3467861.3467877 · Cited by 54 · see citing works

Deep Learning Research and Development Platform: Characterizing and Scheduling with QoS Guarantees on GPU Clusters

Chen, Z., Quan, W., Wen, M., Fang, J., Yu, J., Zhang, C., & Luo, L. (2019) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2019.2931558 · Cited by 54 · see citing works

Online Auctions in IaaS Clouds

Zhang, X., Huang, Z., Wu, C., Li, Z., & Lau, F. C. M. (2015) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/2745844.2745855 · Cited by 53 · see citing works

Network-Aware Scheduling for Data-Parallel Jobs

Jalaparti, V., Bodík, P., Menache, I., Rao, S., Makarychev, K., & Caesar, M. (2015) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2829988.2787488 · Cited by 52 · see citing works

Improving spark application throughput via memory aware task co-location

Marco, V. S., Taylor, B., Porter, B., & Wang, Z. (2017) · International Middleware Conference · doi:10.1145/3135974.3135984 · Cited by 49 · see citing works

Joint Minimization of the Energy Costs From Computing, Data Transmission, and Migrations in Cloud Data Centers

Canali, C., Chiaraviglio, L., Lancellotti, R., & Shojafar, M. (2018) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2018.2796613 · Cited by 48 · see citing works

Scheduling Beyond CPUs for HPC

Fan, Y., Lan, Z., Rich, P., Allcock, W. E., Papka, M. E., Austin, B., & Paul, D. (2019) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/3307681.3325401 · Cited by 48 · see citing works

Scheduling of Time-Varying Workloads Using Reinforcement Learning

Mondal, S. S., Sheoran, N., & Mitra, S. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17088 · Cited by 47 · see citing works

New Scheduling Algorithms for Improving Performance and Resource Utilization in Hadoop YARN Clusters

Yao, Y., Gao, H., Wang, J., Sheng, B., & Ningfang Mi, A. (2019) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2019.2894779 · Cited by 46 · see citing works

DRAS: Deep Reinforcement Learning for Cluster Scheduling in High Performance Computing

Fan, Y., Li, B., Favorite, D., Singh, N., Childers, J. T., Rich, P., Allcock, W. E., Papka, M. E., & Lan, Z. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3205325 · Cited by 47 · see citing works

ERA

Babaioff, M., Timnat, E., Mansour, Y., Nisan, N., Noti, G., Curino, C., Ganapathy, N., Menache, I., Reingold, O., & Tennenholtz, M. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3054186 · Cited by 44 · see citing works

Distributed resource management across process boundaries

Suresh, L., Bodík, P., Menache, I., Canini, M., & Ciucu, F. (2017) · Symposium on Cloud Computing · doi:10.1145/3127479.3132020 · Cited by 44 · see citing works

Randomized Algorithms for Scheduling Multi-Resource Jobs in the Cloud

Psychas, K., & Ghaderi, J. (2018) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2018.2863647 · Cited by 43 · see citing works

Kairos

Delgado, P. O., Didona, D., Dinu, F., & Willy Zwaenepoel, Y. (2018) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3267809.3267838 · Cited by 43 · see citing works

Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and ToolsPreprint

Mayer, R., & Jacobsen, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.11314 · Cited by 42 · see citing works

Reliability-Aware Task Processing and Offloading for Data-Intensive Applications in Edge computing

Liang, J., Ma, B., Feng, Z., & Huang, J. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3258191 · Cited by 41 · see citing works

Bistro: scheduling data-parallel jobs against live production systems

Goder, A., Spiridonov, A., & Wang, Y. (2015) · USENIX Annual Technical Conference · Cited by 41 · see citing works

ROSE: Cluster Resource Scheduling via Speculative Over-Subscription

Sun, X., Hu, C., Yang, R., Garraghan, P., Wo, T., Xu, J. J., Zhu, J., & Li, C. (2018) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2018.00096 · Cited by 41 · see citing works

Yugong

Huang, Y., Shi, Y., Zhong, Z., Feng, Y., Cheng, J. S.-C., Li, J., Fan, H., Li, C., Guan, T., & Zhou, J. (2019) · Proceedings of the VLDB Endowment · doi:10.14778/3352063.3352132 · Cited by 38 · see citing works

Multi-resource balance optimization for virtual machine placement in cloud data centers

Wei, W., Wang, K., Wang, K., Gu, H., & Shen, H. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106866 · Cited by 38 · see citing works

Correlation Modeling and Resource Optimization for Cloud Service With Fault Recovery

Qiu, X., Dai, Y., Xiang, Y., & Xing, L. (2017) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2017.2691323 · Cited by 37 · see citing works

Improved Artificial Bee Colony Using Monarchy Butterfly Optimization Algorithm for Load Balancing (IABC-MBOA-LB) in Cloud Environments

Janakiraman, S., & Priya, M. D. (2021) · Journal of Network and Systems Management · doi:10.1007/s10922-021-09602-y · Cited by 37 · see citing works

RLSchert: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction

Wang, Q., Zhang, H., Qu, C., Shen, Y., Liu, X., & Li, J. (2021) · Applied Sciences · doi:10.3390/app11209448 · Cited by 36 · see citing works

An Exploration of Designing a Hybrid Scale-Up/Out Hadoop Architecture Based on Performance Measurements

Li, Z., Shen, H., Ligon, W. B., III, & Denton, J. (2016) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2016.2573820 · Cited by 36 · see citing works

Understanding and Optimizing Workloads for Unified Resource Management in Large Cloud Platforms

Lu, C., Xu, H., Ye, K., Xu, G., Zhang, L., Yang, G., & Xu, C. (2023) · European Conference on Computer Systems · doi:10.1145/3552326.3587437 · Cited by 35 · see citing works

AlphaR: Learning-Powered Resource Management for Irregular, Dynamic Microservice Graph

Hou, X., Li, C., Liu, J., Zhang, L., Ren, S., Leng, J., Chen, Q., & Guo, M. (2021) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps49936.2021.00089 · Cited by 35 · see citing works

Pandas: Robust Locality-Aware Scheduling With Stochastic Delay Optimality

Xie, Q., Pundir, M., Lu, Y., Abad, C. L., & Campbell, R. H. (2016) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2016.2606900 · Cited by 34 · see citing works

An Efficient Online Placement Scheme for Cloud Container Clusters

Zhou, R., Li, Z., & Wu, C. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2906745 · Cited by 34 · see citing works

SCARL: Attentive Reinforcement Learning-Based Scheduling in a Multi-Resource Heterogeneous Cluster

Cheong, M., Lee, H., Yeom, I., & Woo, H. (2019) · IEEE Access · doi:10.1109/access.2019.2948150 · Cited by 34 · see citing works

Resource Management in Cloud Computing Using Machine Learning: A Survey

Goodarzy, S., Nazari, M., Han, R., Keller, E., & Rozner, E. (2020) · International Conference on Machine Learning and Applications · doi:10.1109/icmla51294.2020.00132 · Cited by 27 · see citing works

Switches for HIRE: resource scheduling for data center in-network computing

Blöcher, M., Wang, L., Eugster, P. T., & Schmidt, M. (2021) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3445814.3446760 · Cited by 34 · see citing works

Deadline-constrained workflow scheduling in IaaS clouds with multi-resource packing

Zhu, Z., & Tang, X. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.07.043 · Cited by 33 · see citing works

Dependency-Aware and Resource-Efficient Scheduling for Heterogeneous Jobs in Clouds

Liu, J., & Shen, H. (2016) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom.2016.0032 · Cited by 33 · see citing works

Deep Learning-Based Job Placement in Distributed Machine Learning Clusters With Heterogeneous Workloads

Bao, Y., Peng, Y., & Wu, C. (2022) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2022.3202529 · Cited by 32 · see citing works

Tempo

Tan, Z., & Babu, S. (2016) · Proceedings of the VLDB Endowment · doi:10.14778/2977797.2977799 · Cited by 32 · see citing works

Performance-Aware Speculative Resource Oversubscription for Large-Scale Clusters

Yang, R., Hu, C., Sun, X., Garraghan, P., Wo, T., Wen, Z., Peng, H. M., Xu, J., & Li, C. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.2970013 · Cited by 32 · see citing works

GARLSched: Generative adversarial deep reinforcement learning task scheduling optimization for large-scale high performance computing systems

Li, J., Zhang, X., Wei, J., Ji, Z., & Wei, Z. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.04.032 · Cited by 33 · see citing works

EdgeTuner: Fast Scheduling Algorithm Tuning for Dynamic Edge-Cloud Workloads and Resources

Han, R., Wen, S., Liu, C. H., Yuan, Y., Wang, G., & Chen, L. Y. (2022) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom48880.2022.9796792 · Cited by 31 · see citing works

Fair multi-resource allocation with external resource for mobile edge computing

Meskar, E., & Liang, B. (2018) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2018.8406965 · Cited by 31 · see citing works

Data Centers Job Scheduling with Deep Reinforcement Learning

Liang, S., Yang, Z., Jin, F., & Chen, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-47436-2_68 · Cited by 30 · see citing works

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 · see citing works

Altruistic scheduling in multi-resource clusters

Grandl, R., Chowdhury, M., Akella, A., & Ananthanarayanan, G. (2016) · Operating Systems Design and Implementation · doi:10.5555/3026877.3026884 · Cited by 139 · see citing works

Graphene: packing and dependency-aware scheduling for data-parallel clusters

Grandl, R., Kandula, S., Rao, S., Akella, A., & Kulkarni, J. (2016) · Operating Systems Design and Implementation · doi:10.5555/3026877.3026885 · Cited by 155 · see citing works

Best Effort Task Scheduling for Data Parallel Jobs

Li, Z., Zhang, Y., Zhao, Y., Peng, Y., & Li, D. (2016) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/2934872.2959047 · Cited by 9 · see citing works

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 · see citing works