Large-scale cluster management at Google with Borg

MapReduce: simplified data processing on large clusters

Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476

Pregel

Malewicz, G. G., Austern, M. H., Bik, A. J. C., Dehnert, J. C., Horn, I., Leiser, N., & Czajkowski, G. (2010) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1807167.1807184 · Cited by 3,518

Bigtable

Chang, F. W., Dean, J., Ghemawat, S., Hsieh, W. C.-Y., Wallach, D. A., Burrows, M., Chandra, T., Fikes, A., & Gruber, R. E. (2008) · ACM Transactions on Computer Systems · doi:10.1145/1365815.1365816 · Cited by 3,461

The part-time parliament

Lamport, L. (1998) · ACM Transactions on Computer Systems · doi:10.1145/279227.279229 · Cited by 2,748

The Google file system

Ghemawat, S., Gobioff, H., & Leung, S.-T. A. (2003) · ACM SIGOPS Operating Systems Review · doi:10.1145/1165389.945450 · Cited by 2,665

Condor-a hunter of idle workstations

Litzkow, M., Livny, M., & Mutka, M. W. (2003) · Proceedings of the ... International Conference on Distributed Computing Systems/Proceedings of the International Conference on Distributed Computing Systems · doi:10.1109/dcs.1988.12507 · Cited by 2,243

The tail at scale

Dean, J., & Barroso, L. A. (2013) · Communications of the ACM · doi:10.1145/2408776.2408794 · Cited by 1,852

Apache Hadoop YARN: yet another resource negotiator

Vavilapalli, V. K., Murthy, A. C., Douglas, C., Agarwal, S., Konar, M., Evans, R., Graves, T., Lowe, J., Shah, H., Seth, S., et al. (2013) · Proceedings of the 4th annual Symposium on Cloud Computing · doi:10.1145/2523616.2523633 · Cited by 1,848

The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines

Barroso, L. A., & Hölzle, U. (2009) · Synthesis lectures on computer architecture · doi:10.2200/s00193ed1v01y200905cac006 · Cited by 1,711

Untitled work

(2011) · doi:10.5555/1972457.1972488 · Cited by 1,619

Evaluating Raft in Docker on Kubernetes

Oliveira, C. P., Lung, L. C., Netto, H. V., et al. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-48944-5_12 · Cited by 16

Rack-level Congestion Control

Zhuo, D., Zhang, Q., Liu, V., et al. (2016) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/3005745.3005772 · Cited by 16

Computing in the Continuum: Combining Pervasive Devices and Services to Support Data-Driven Applications

AbdelBaky, M., Zou, M., Zamani, A. R., et al. (2017) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.323 · Cited by 16

DC-Prophet: Predicting Catastrophic Machine Failures in DataCenters

Lee, Y.-L., Juan, D.-C., Tseng, X.-A., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71273-4_6 · Cited by 16

On Non-Preemptive VM Scheduling in the Cloud

Psychas, K., & Ghaderi, J. (2018) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/3219617.3219644 · Cited by 16

Evaluation of Container Orchestration Systems for Deploying and Managing NoSQL Database Clusters

Truyen, E., Bruzek, M., Landuyt, D. V., et al. (2018) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2018.00066 · Cited by 16

Scheduling Jobs with Random Resource Requirements in Computing Clusters

Psychas, K., & Ghaderi, J. (2019) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2019.8737612 · Cited by 16

Goldilocks: Adaptive Resource Provisioning in Containerized Data Centers

Zhou, L., Bhuyan, L. N., & Ramakrishnan, K. K. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00072 · Cited by 16

Modeling The Temporally Constrained Preemptions of Transient Cloud VMs

Kadupitige, J., Jadhao, V., & Sharma, P. (2020) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/3369583.3392671 · Cited by 16

Skynet: Performance-driven Resource Management for Dynamic Workloads

Sfakianakis, Y., Marazakis, M., & Bilas, A. (2021) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud53861.2021.00069 · Cited by 16

SOL: safe on-node learning in cloud platforms

Wang, Y., Crankshaw, D., Yadwadkar, N. J., et al. (2022) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3503222.3507704 · Cited by 16

CASE

Chen, C., Porter, C., & Pande, S. (2022) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/3503221.3508423 · Cited by 16

Reducing response latency of composite functions-as-a-service through scheduling

Żuk, P., & Rzaḑca, K. (2022) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2022.04.011 · Cited by 16

E-HPC

Fox, W., Ghoshal, D., Souza, A., et al. (2017) · Workshop on Workflows in Support of Large-Scale Science (WORKS) · doi:10.1145/3150994.3150996 · Cited by 16

Sage: Leveraging ML to Diagnose Unpredictable Performance in Cloud MicroservicesPreprint

Gan, Y., Liang, M., Dev, S., Lo, D., & Delimitrou, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.06263 · Cited by 16

AlphaEvolve: A coding agent for scientific and algorithmic discovery

Novikov, A., Vũ, N., Eisenberger, M., Dupont, E., Huang, P.-S., Wagner, A. Z., Shirobokov, S., Kozlovskii, B., Ruiz, F. J. R., Mehrabian, A., et al. (2025) · arXiv · doi:10.48550/arxiv.2506.13131 · Cited by 15

Risk based planning of network changes in evolving data centers

Alipourfard, O., Gao, J., Koenig, J., Harshaw, C., Vahdat, A., & Yu, M. (2019) · Proceedings of the 27th ACM Symposium on Operating Systems Principles · doi:10.1145/3341301.3359664 · Cited by 18

Task Scheduling in Geo-Distributed Computing: A Survey

Wu, Y., Tang, S., Yu, C., Yang, B., Sun, C., Xiao, J. K., Wu, H., & Feng, J. (2025) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2025.3591010 · Cited by 15

Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum

Alonso, J., Orue-Echevarria, L., Osaba, E., et al. (2021) · Information · doi:10.3390/info12080308 · Cited by 15

Demeter

Tang, W., Ke, Y., Fu, S., et al. (2022) · Symposium on Cloud Computing · doi:10.1145/3542929.3563476 · Cited by 15

MRSch: Multi-Resource Scheduling for HPC

Li, B., Fan, Y., Dearing, M. T., Lan, Z., Rich, P., Allcock, W. E., & Papka, M. E. (2022) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster51413.2022.00020 · Cited by 15

A Hierarchical Namespace Approach for Multi-Tenancy in Distributed Clouds

Simić, M., Dedeić, J., Stojkov, M., & Prokić, I. (2024) · IEEE Access · doi:10.1109/access.2024.3369031 · Cited by 16

LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand

Bostandoost, R., Lechowicz, A., Hanafy, W. A., Bashir, N., Shenoy, P. J., & Hajiesmaili, M. (2024) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3632775.3661942 · Cited by 16

Tetris: Proactive Container Scheduling for Long-Term Load Balancing in Shared Clusters

Xu, F., Shen, X., Lin, S.-H., Chen, L., Zhou, Z., Xiao, F., & Liu, F. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3442544 · Cited by 15

Dynamic Proportional Sharing

Freeman, R., Zahedi, S. M., Conitzer, V., et al. (2018) · doi:10.1145/3219617.3219631 · Cited by 15

Decoupling the control plane from program control flow for flexibility and performance in cloud computing

Qu, H., Mashayekhi, O., Shah, C., et al. (2018) · doi:10.1145/3190508.3190516 · Cited by 15

Portfolio-driven Resource Management for Transient Cloud Servers

Sharma, P., Irwin, D., & Shenoy, P. J. (2017) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3084442 · Cited by 15

Efficient Implementation of the Room Simulator for Training Deep Neural Network Acoustic Models

Kim, C., Variani, E., Narayanan, A., & Bacchiani, M. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-2566 · Cited by 19

Exploring Characteristics of Inter-cluster Machines and Cloud Applications on Google Clusters

Lin, Y., Barker, A., & Ceesay, S. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9377802 · Cited by 15

SoK: Function-As-A-Service: From An Application Developer’s Perspective

Raza, A., Matta, I., Akhtar, N., Kalavri, V., & Isahagian, V. (2021) · Journal of Systems Research · doi:10.5070/sr31154815 · Cited by 15

OS-Augmented Oversubscription of Opportunistic Memory with a User-Assisted OOM Killer

Chen, W., Pi, A., Wang, S., et al. (2019) · International Middleware Conference · doi:10.1145/3361525.3361534 · Cited by 15

We need kernel interposition over the network dataplane

Sadok, H., Zhao, Z., Choung, V., et al. (2021) · Workshop on Hot Topics in Operating Systems · doi:10.1145/3458336.3465281 · Cited by 15

Energy-Cost-Driven Scheduling Scheme for Data Centers Incorporating Job Dependency Constraints

Li, X., Wang, J., Shi, K., et al. (2025) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2025.3546587 · Cited by 14

Proactive AI-and-RAN Workload Orchestration in O-RAN Architectures for 6G Networks

Shah, S. D. A., Hafeez, M., Salama, A., et al. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3608700 · Cited by 14

A multi-resource scheduling scheme of Kubernetes for IIoT

Zhu, L., Li, J., Liu, Z., et al. (2022) · Journal of Systems Engineering and Electronics · doi:10.23919/jsee.2022.000063 · Cited by 14

Unifying serverless and microservice workloads with SigmaOS

Szekely, A., Belay, A., Morris, R., & Kaashoek, M. F. (2024) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3694715.3695947 · Cited by 14

Is Trust Misplaced? A Zero-Trust Survey

Poirrier, A., Cailleux, L., & Clausen, T. (2025) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3555131 · Cited by 21

Awan: Locality-Aware Resource Manager for Geo-Distributed Data-Intensive Applications

Jonathan, A., Chandra, A., & Weissman, J. (2016) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e.2016.15 · Cited by 15

A Framework for Enabling Security Services Collaboration Across Multiple Domains

Migault, D., Junior, M. A. S., Barros, B. M., et al. (2017) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.67 · Cited by 14

Productive Efficiency of Energy-Aware Data Centers

Fernández-Cerero, D., Fernández-Montes, A., & Morente, F. V. (2018) · Energies · doi:10.3390/en11082053 · Cited by 14

Spread-n-share

Tang, X., Wang, H., Ma, X., et al. (2019) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3295500.3356152 · Cited by 14

Le Taureau: Deconstructing the Serverless Landscape & A Look Forward

Khandelwal, A., Kejariwal, A., & Ramasamy, K. (2020) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3318464.3383130 · Cited by 19

Less Provisioning: A Hybrid Resource Scaling Engine for Long-Running Services With Tail Latency Guarantees

Cai, B., Li, K., Zhao, L., et al. (2020) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2020.3016345 · Cited by 14

Comparative experimental analysis of Docker container networking drivers

Mentz, L. L., Loch, W. J., & Koslovski, G. (2020) · IEEE International Conference on Cloud Networking · doi:10.1109/cloudnet51028.2020.9335811 · Cited by 14

Phoebe: QoS-Aware Distributed Stream Processing through Anticipating Dynamic Workloads

Geldenhuys, M. K., Scheinert, D., Kao, O., et al. (2022) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws55610.2022.00041 · Cited by 14

SKALD: A Scalable Architecture for Feature Extraction, Multi-user Analysis, and Real-Time Information Sharing

Webster, G. D., Hanif, Z. D., Ludwig, A., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45871-7_15 · Cited by 14

Amdahl's Law in the Datacenter Era: A Market for Fair Processor Allocation

Zahedi, S. M., Llull, Q., & Lee, B. C. (2018) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca.2018.00011 · Cited by 14

Effective Capacity Modulation as an Explicit Control Knob for Public Cloud Profitability

Wang, C., Urgaonkar, B., Kesidis, G., et al. (2018) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3139291 · Cited by 14

Optimal Job Scheduling With Resource Packing for Heterogeneous Servers

Xu, H., Liu, Y., & Lau, W. C. (2021) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2021.3068201 · Cited by 14

Machine learning-based cloud resource allocation algorithms: a comprehensive comparative review

Bodra, D., & Khairnar, S. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1678976 · Cited by 31

A Survey on Computing Power Networks: Architecture, Resource Allocation, and AI Services Enablement

Zhao, W., Yu, Y., Mao, B., & Kato, N. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3655188 · Cited by 13

Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction

Liu, C., Jiao, J., Li, W., et al. (2022) · Entropy · doi:10.3390/e24121770 · Cited by 13

Gödel

Xiang, W., Li, Y., Ren, Y., et al. (2023) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3620678.3624663 · Cited by 13

Dirigent: Lightweight Serverless Orchestration

Cvetković, L., Costa, F., Djokic, M., et al. (2024) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3694715.3695966 · Cited by 13

DCMPTCP: Host-Based Load Balancing for Datacenters

Dong, E., Fu, X., Xu, M., et al. (2018) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2018.00067 · Cited by 13

Building the SLATE Platform

Breen, J., Bryant, L., Carcassi, G., et al. (2018) · Practice and Experience in Advanced Research Computing · doi:10.1145/3219104.3219144 · Cited by 13

Towards Deep Network & Application Integration

Peréz, D. A. L., Xiang, Q., Rothenberg, C. E., et al. (2020) · doi:10.1145/3405672.3405804 · Cited by 13

Towards Scalable Resource Management for Supercomputers

Dai, Y., Dong, Y. Q., Lü, K., et al. (2022) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41404.2022.00029 · Cited by 13

Eigen: End-to-End Resource Optimization for Large-Scale Databases on the Cloud

Li, J. Y., Zhang, J., Zhou, W., Liu, Y., Zhang, S., Xue, Z., Xu, D., Fan, H. T., Zhou, F., & Li, F. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3611540.3611565 · Cited by 13

BigVM: A Multi-Layer-Microservice-Based Platform for Deploying SaaS

Zheng, T., Zhang, Y., Zheng, X., et al. (2017) · International Conference on Advanced Cloud and Big Data · doi:10.1109/cbd.2017.16 · Cited by 13

Per-Application Power Delivery

Guliani, A., & Swift, M. M. (2019) · doi:10.1145/3302424.3303981 · Cited by 13

Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

Kilanioti, I., Fernández-Montes, A., Fernández-Cerero, D., Karageorgos, A., Mettouris, C., Nejković, V., Albanis, N., Bashroush, R., & Papadopoulos, G. A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16272-6_4 · Cited by 13

A Case for Performance-Aware Deployment of Containers

Boza, E. F., Abad, C. L., Narayanan, S. P., Balasubramanian, B., & Jang, M. (2019) · International Workshop on Container Technologies and Container Clouds (WoC) · doi:10.1145/3366615.3368355 · Cited by 13

Low-Cost Datacenter Load Balancing With Multipath Transport and Top-of-Rack Switches

Dong, E., Fu, X., Xu, M., et al. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.2989441 · Cited by 13

N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Improve Docker Performance

Gu, L., Tang, Q., Wu, S., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30709-7_15 · Cited by 13

Don't cry over spilled records: Memory elasticity of data-parallel applications and its application to cluster schedulingPreprint

Iorgulescu, C., Dinu, F., Raza, A., Hassan, W. U., & Willy Zwaenepoel, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.04323 · Cited by 13

To Centralize or Not to Centralize: A Tale of Swarm CoordinationPreprint

Hu, J., Bruno, A., Zagieboylo, D., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.01786 · Cited by 13

The Ideal Versus the Real

Randal, A. (2020) · ACM Computing Surveys · doi:10.1145/3365199 · Cited by 12

A Deep Dive into the Google Cluster Workload Traces: Analyzing the Application Failure Characteristics and User Behaviors

Bappy, F. H., Islam, T., Zaman, T. S., et al. (2023) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud58648.2023.00023 · Cited by 12

A Decentralized SDN Architecture for the WAN

Krentsel, A., Saran, N., Koley, B., et al. (2024) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3651890.3672257 · Cited by 12

SmartOClock: Workload- and Risk-Aware Overclocking in the Cloud

Stojkovic, J., Misra, P. A., Goiri, Í., Whitlock, S., Choukse, E., Das, M., Bansal, C., Lee, J., Sun, Z., Qiu, H., et al. (2024) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca59077.2024.00040 · Cited by 12

BREAK: A Holistic Approach for Efficient Container Deployment among Edge Clouds

Feng, Y., Shen, S., Wang, X., et al. (2024) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom52122.2024.10621084 · Cited by 12

MuxFlow: efficient GPU sharing in production-level clusters with more than 10000 GPUs

Liu, X., Zhao, Y., Liu, S., Li, X., Zhu, Y., Liu, X., & Jin, X. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4227-2 · Cited by 12

SCoPe: A Decision System for Large Scale Container Provisioning Management

Hegde, A., Ghosh, R., Mukherjee, T., et al. (2016) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2016.0038 · Cited by 12

Optimal Resource Allocation for Elastic and Inelastic Jobs

Berg, B., Harchol‐Balter, M., Moseley, B., et al. (2020) · ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) · doi:10.1145/3350755.3400265 · Cited by 12

μSteal

Mirhosseini, A., & Wenisch, T. F. (2021) · International Conference on Supercomputing · doi:10.1145/3447818.3463529 · Cited by 12

Fast and Fine-grained Autoscaler for Streaming Jobs with Reinforcement Learning

Xing, M., Mao, H., & Xiao, Z. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/80 · Cited by 12

GreenDRL

Zhang, K., Wang, P., Gu, N., & Nguyen, T. D. (2022) · Symposium on Cloud Computing · doi:10.1145/3542929.3563501 · Cited by 13

tf.data service

Audibert, A., Chen, Y., Graur, D., Klimovic, A., Šimša, J., & Thekkath, C. A. (2023) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3620678.3624666 · Cited by 25

PortHadoop: Support direct HPC data processing in Hadoop

Yang, X., Liu, N., Feng, B., et al. (2015) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2015.7363759 · Cited by 12

How Heterogeneity Affects the Design of Hadoop MapReduce Schedulers: A State-of-the-Art Survey and Challenges

Pandey, V., & Saini, P. (2018) · Big Data · doi:10.1089/big.2018.0013 · Cited by 12

Cloud-scale VM-deflation for Running Interactive Applications On Transient Servers

Fuerst, A., Ali-Eldin, A., Shenoy, P. J., & Sharma, P. (2020) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/3369583.3392675 · Cited by 12

FIXME: Enhance Software Reliability with Hybrid Approaches in Cloud

Hwang, J., Shwartz, L., Wang, Q., Batta, R., Kumar, H., & Nidd, M. (2021) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip52600.2021.00032 · Cited by 13

Efficient Flow-Based Scheduling for Geo-Distributed Simulation Tasks in Collaborative Edge and Cloud Environments

Zhang, M., Yong, P., Zhu, J., et al. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3155713 · Cited by 12

Unicorn: Unified resource orchestration for multi-domain, geo-distributed data analytics

Xiang, Q., Wang, X. T., Zhang, J., et al. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.09.048 · Cited by 12

The Ideal Versus the Real: Revisiting the History of Virtual Machines and ContainersPreprint

Randal, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.12226 · Cited by 12

On Universal Scaling of Distributed Queues under Load BalancingPreprint

Liu, X., & Ying, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11904 · Cited by 12

High-Performance Big Data Computing

Panda, D. K. D., Lü, X., & Shankar, D. (2022) · The MIT Press eBooks · doi:10.7551/mitpress/11451.001.0001 · Cited by 11

A Comparison of Machine Learning-Based and Conventional Technologies for Video Compression

Mochurad, L. (2024) · Technologies · doi:10.3390/technologies12040052 · Cited by 11

Scientific workflow scheduling algorithms in cloud environments: a comprehensive taxonomy, survey, and future directions

Saeedizade, E., & Ashtiani, M. (2024) · Journal of Scheduling · doi:10.1007/s10951-024-00820-1 · Cited by 11

Delay comparison of delivery and coding policies in data clusters

Shah, V., Bouillard, A., & Baccelli, F. (2017) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2017.8262765 · Cited by 11

A Generic Framework Supporting Self-Organisation and Self-Management in Hierarchical Systems

Filelis‐Papadopoulos, C. K., Xiong, H., Spătaru, A., et al. (2017) · International Symposium on Parallel and Distributed Computing · doi:10.1109/ispdc.2017.18 · Cited by 11

The Mystery of the Failing Jobs: Insights from Operational Data from Two University-Wide Computing Systems

Kumar, R., Jha, S., Mahgoub, A., et al. (2020) · IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) · doi:10.1109/dsn48063.2020.00034 · Cited by 11

A review on orchestration distributed systems for IoT smart services in fog computing

Pakhrudin, N. S. M., Kassim, M. B., & Idris, A. (2021) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v11i2.pp1812-1822 · Cited by 11

Web-based multi-user concurrent job scheduling system on the shared computing resource objects

Habuza, T., Khalil, K. M., Zaki, N., et al. (2020) · International Conference on Innovations in Information Technology · doi:10.1109/iit50501.2020.9299110 · Cited by 11

KOLE

Zhang, J., Chen, J., Huang, Y., Li, Y., Ding, Y., & Guo, F. (2022) · Symposium on Cloud Computing · doi:10.1145/3542929.3563462 · Cited by 11

Genetic algorithm with skew mutation for heterogeneous resource-aware task offloading in edge-cloud computing

Chen, M., Qi, P., Chu, Y., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e32399 · Cited by 11

Robust and Probabilistic Failure-Aware Placement

Korupolu, M. R., & Rajaraman, R. (2016) · ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) · doi:10.1145/2935764.2935802 · Cited by 11

Maximizing container-based network isolation in parallel computing clusters

Ma, S., Jiang, J., Li, B., et al. (2016) · Proceedings/Proceedings - International Conference on Network Protocols · doi:10.1109/icnp.2016.7784434 · Cited by 11

iCSI: A Cloud Garbage VM Collector for Addressing Inactive VMs with Machine Learning

Kim, I. K., Zeng, S., Young, C., et al. (2017) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e.2017.28 · Cited by 11

ИСПОЛЬЗОВAНИЕ ПОТЕНЦИAЛA СОЦИAЛЬНЫХ ПAРТНЕРОВ В ПОДГОТОВКЕ БУДУЩИХ ПЕДAГОГОВ

Атабекова, Б. Б. (2024) · Habaršy. Pedagogika ġylymdary seriâsy · doi:10.51889/2959-5762.2023.80.4.019

DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION

Mustafa, N. H., Kelesbayev, D., & Alibekova, Z. D. (2018) · The Journal of Economic Research & Business Administration · doi:10.26577/be-2018-2-2141

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Using DataGrid Control to Realize DataBase of Querying in VB6.0

Hu, S. (2000) · Modern Computer

ESKVS: efficient and secure approach for keyframes-based video summarization framework

Saini, P., & Berwal, K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18405-7 · Cited by 10