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

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Scheduling Recurring Distributed Dataflow Jobs Based on Resource Utilization and Interference

Thamsen, L., Rabier, B., Schmidt, F., Renner, T., & Kao, O. (2017) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2017.28 · Cited by 11

Resource Utilization Analysis of Alibaba Cloud

Deng, L. X., Ren, Y.-L., Xu, F., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-95930-6_18 · Cited by 11

Less Provisioning

Cai, B., Zhang, R., Zhao, L., et al. (2018) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3225058.3225113 · Cited by 11

Caravel: Burst Tolerant Scheduling for Containerized Stateful Applications

Deshpande, U. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00143 · Cited by 11

A Case for Adaptive Resource Management in Alibaba Datacenter Using Neural Networks

Wang, S., Zhu, Y.-H., Chen, S.-P., et al. (2020) · Journal of Computer Science and Technology · doi:10.1007/s11390-020-9732-x · Cited by 11

PADL: A Modeling and Deployment Language for Advanced Analytical Services

Díaz-de-Arcaya, J., Miñón, R., Torre-Bastida, A. I., Ser, J. D., & Almeida, A. (2020) · Sensors · doi:10.3390/s20236712 · Cited by 11

DPCLS: Improving Partial Cache Line Sparing with Dynamics for Memory Error Prevention

Du, X., & Li, C. (2020) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd50377.2020.00045 · Cited by 11

Chiron: Optimizing Fault Tolerance in QoS-aware Distributed Stream Processing Jobs

Geldenhuys, M. K., Thamsen, L., & Kao, O. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378474 · Cited by 11

QoS for best‐effort batch jobs in container‐based cloud

Yim, Y.-G., Jang, H., & Jin, H. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6422 · Cited by 11

Optimizing the Cost of Executing Mixed Interactive and Batch Workloads on Transient VMs

Ambati, P., & Irwin, D. (2019) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3341617.3326143 · Cited by 11

OpERA: Opportunistic and Efficient Resource Allocation in Hadoop YARN by Harnessing Idle Resources

Yao, Y., Gao, H., Wang, J., et al. (2016) · International Conference on Computer Communication and Networks (ICCCN) · doi:10.1109/icccn.2016.7568553 · Cited by 11

A Droplet Approach Based on Raptor Codes for Distributed Computing With Straggling Servers

Severinson, A., Amat, A. G. I., Rosnes, E., Lázaro, F., & Liva, G. (2018) · International Symposium on Turbo Codes and Iterative Information Processing · doi:10.1109/istc.2018.8625292 · Cited by 11

Fair and Efficient Memory Sharing: Confronting Free Riders

Friedman, E. J., Gkatzelis, V., Psomas, A., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011965 · Cited by 11

Cloud-Native Computing: A Survey from the Perspective of ServicesPreprint

Deng, S., Zhao, H., Huang, B., Zhang, C., Chen, F., Deng, Y., Yin, J., Dustdar, S., & Zomaya, A. Y. (2023) · doi:10.36227/techrxiv.23500383 · Cited by 11

Mitigating interference of microservices with a scoring mechanism in large-scale clusters

Yang, D., Zheng, K., Qian, S., et al. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06534-7 · Cited by 10

DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud

Wang, Q., Lan, T., Tang, Y., Sang, B., Huang, Z., Du, Y., Zhang, H., Sha, J., Lu, H., Zhou, Y., et al. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3685800.3685832 · Cited by 12

The Impact of Service Demand Variability on Data Center Performance

Olliaro, D., Anggraito, A., Marsan, M. A., et al. (2024) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2024.3497792 · Cited by 10

Optimizing Kubernetes Network Performance: A Study of Container Network Interfaces and System Tuning Profiles

Chippagiri, S. (2024) · European Journal of Theoretical and Applied Sciences · doi:10.59324/ejtas.2024.2(6).58 · Cited by 10

Coach: Exploiting Temporal Patterns for All-Resource Oversubscription in Cloud Platforms

Reidys, B., Zardoshti, P., Goiri, Í., Irvene, C., Berger, D. S., Ma, H., Arya, K., Cortez, E., Stark, T., Bak, E., et al. (2025) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3669940.3707226 · Cited by 10

Hardware Sentinel: Protecting Software Applications from Hardware Silent Data Corruptions

Dutta, R., Dixit, H. D., van Riel, R., et al. (2025) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3676641.3716258 · Cited by 10

Pluggable scheduling for the reactor programming model

Prokopec, A. (2016) · doi:10.1145/3001886.3001891 · Cited by 10

Cluster as a service: A resource sharing approach for private cloud

Cao, D., Liu, P., Cui, W., et al. (2016) · Tsinghua Science & Technology · doi:10.1109/tst.2016.7787004 · Cited by 10

Multi-dimensional Admission Control and Capacity Planning for IaaS Clouds with Multiple Service Classes

Carvalho, M., Brasileiro, F., Lopes, R., et al. (2017) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid.2017.14 · Cited by 10

A Survey of System Scheduling for HPC and Big Data

Wang, B. Y., Chen, Z., & Xiao, N. (2020) · International Conference on High Performance Compilation, Computing and Communications (HP3C) · doi:10.1145/3407947.3407977 · Cited by 10

Navigator: Dynamic Multi-kernel Scheduling to Improve GPU Performance

Kim, J., Kim, J., & Park, Y. (2020) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18072.2020.9218711 · Cited by 10

TOPOSCH: Latency-Aware Scheduling Based on Critical Path Analysis on Shared YARN Clusters

Hu, C., Zhu, J., Yang, R., et al. (2020) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud49709.2020.00091 · Cited by 10

Decentralized Low-Latency Task Scheduling for Ad-Hoc Computing

Edinger, J., Breitbach, M., Gabrisch, N., et al. (2021) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps49936.2021.00087 · Cited by 10

Mind the Gap

Liu, L., Wang, H., Wang, A., Xiao, M., Cheng, Y., & Chen, S. (2021) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3472883.3486997 · Cited by 10

Recent implications towards sustainable and energy efficient AI and big data implementations in cloud-fog systems: A newsworthy inquiry

Hamzaoui, I., Duthil, B., Courboulay, V., & Medromi, H. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.11.002 · Cited by 10

Tenant-Grained Request Scheduling in Software-Defined Cloud Computing

Tu, H., Zhao, G., Xu, H., et al. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3199031 · Cited by 10

Real-Time Analytics: Benefits, Limitations, and Tradeoffs

Kuznetsov, S. D., Velikhov, P., & Fu, Q. (2023) · Programming and Computer Software · doi:10.1134/s036176882301005x · Cited by 10

Blox: A Modular Toolkit for Deep Learning Schedulers

Agarwal, S., Phanishayee, A., & Venkataraman, S. (2024) · European Conference on Computer Systems · doi:10.1145/3627703.3629583 · Cited by 13

Daphne: A Flexible and Hybrid Scheduling Framework in Multi-Tenant Clusters

Xia, Y., Ren, R., Cai, H., et al. (2017) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2017.2777885 · Cited by 10

Peacock: Probe-Based Scheduling of Jobs by Rotating Between Elastic Queues

Khelghatdoust, M., & Gramoli, V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-96983-1_13 · Cited by 10

JOVIAL: Notebook-based astronomical data analysis in the cloud

Araya, M., Osorio, M., Díaz, M., et al. (2018) · Astronomy and Computing · doi:10.1016/j.ascom.2018.09.001 · Cited by 10

The Price Is (Not) Right: Reflections on Pricing for Transient Cloud Servers

Irwin, D., Shenoy, P. J., Ambati, P., et al. (2019) · International Conference on Computer Communication and Networks (ICCCN) · doi:10.1109/icccn.2019.8846933 · 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

Long-term Energy Consumption Forecasting for Data Center Industry in China

Liu, W., Mao, H., Cao, Y., Tan, W., Ding, Z., & Wang, P. (2021) · IEEE Conference on Energy Internet and Energy System Integration (EI2) · doi:10.1109/ei252483.2021.9713390 · 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

CHALLENGES OF RESOURCE DISCOVERY TO SUPPORT DISTRIBUTED EXASCALE COMPUTING ENVIRONMENT

Adibi, E., & Khaneghah, E. M. (2018) · Azerbaijan Journal of High Performance Computing · doi:10.32010/26166127.2018.1.2.168.178 · Cited by 10

Workload Characterization of a Software-as-a-Service Web Application Implemented with a Microservices Architecture

Aragon, H., Braganza, S., Boza, E. F., et al. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308560.3316466 · Cited by 10

Rorg: Service Robot Software Management with Linux Containers

Wang, S., Liu, X., Zhao, J., et al. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793764 · Cited by 10

Learning to Diagnose Stragglers in Distributed Computing

Li, C., Shen, H., & Huang, T. (2016) · doi:10.1109/mtags.2016.04 · Cited by 10

An Empirical Study on Kubernetes Operator Bugs

Xu, Q., Gao, Y., & Wei, J. (2024) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3650212.3680396 · Cited by 9

Apex Lake

Metsch, T., Ibidunmoye, O., Bayon-Molino, V., et al. (2015) · doi:10.1145/2830013.2830016 · Cited by 9

Enabling Data Driven Projects for a Modern Enterprise

Topchyan, A. R. (2016) · Proceedings of the Institute for System Programming of RAS · doi:10.15514/ispras-2016-28(3)-13 · Cited by 9

DREAM

Zhou, L., Bhuyan, L. N., & Ramakrishnan, K. K. (2019) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3307772.3328291 · Cited by 9

Elevating the Edge to Be a Peer of the Cloud

Ramachandran, U., Gupta, H., Hall, A., et al. (2019) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2019.00016 · Cited by 9

Approximate QoS Rule Derivation Based on Root Cause Analysis for Cloud Computing

Konno, S., & Défago, X. (2019) · Pacific Rim International Symposium on Dependable Computing · doi:10.1109/prdc47002.2019.00020 · Cited by 7

Deep-Edge: An Efficient Framework for Deep Learning Model Update on Heterogeneous Edge

Bhattacharjee, A., Chhokra, A., Sun, H., Shekhar, S., Gokhale, A. S., Karsai, G., & Dubey, A. K. (2020) · IEEE International Conference on Fog and Edge Computing (ICFEC) · doi:10.1109/icfec50348.2020.00016 · Cited by 9

Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation

Scheinert, D., Zhu, H., Thamsen, L., et al. (2021) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/ipccc51483.2021.9679361 · Cited by 9

Sovereign Cloud Technologies for Scalable Data Spaces

Holfelder, W., Mayer, A., & Baumgart, T. (2022) · Springer eBooks · doi:10.1007/978-3-030-93975-5_25 · Cited by 9

A System for Sustainable Usage of Computing Resources Leveraging Deep Learning Predictions

Cioca, M., & Schuszter, I. C. (2022) · Applied Sciences · doi:10.3390/app12178411 · Cited by 9

An Adaptive Elastic Multi-model Big Data Analysis and Information Extraction System

Yin, Q., Wang, J., Du, S., Leng, J., Li, J., Hong, Y., Zhang, F., Chai, Y., Zhang, X., Zhao, X., et al. (2022) · Data Science and Engineering · doi:10.1007/s41019-022-00196-2 · Cited by 9

Efficient Scheduler Live Update for Linux Kernel with Modularization

Ma, T., Chen, S.-P., Wu, Y., et al. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3582016.3582054 · Cited by 9

LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup

Gu, L., Huang, J., Huang, S., Zeng, D., Li, B., & Jin, H. (2023) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom53939.2023.10229072 · Cited by 9

Demystifying deep learning in predictive monitoring for cloud-native SLOs

Morichetta, A., Pusztai, T., Vij, D., et al. (2023) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud60044.2023.00013 · Cited by 9

A self-stabilizing and auto-provisioning orchestration for microservices in edge-cloud continuum

Cai, B., Wang, X., Wang, B., Yang, M., Guo, Y., & Guo, Q. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110279 · Cited by 10

Data-Driven Workload Generation Based on Google Data Center Measurements

Yildiz, M., & Baiocchi, A. (2024) · IEEE International Conference on High Performance Switching and Routing (HPSR) · doi:10.1109/hpsr62440.2024.10635925 · Cited by 9

Flexible Scheduling of Distributed Analytic Applications

Pace, F., Venzano, D., Damiano Carra, B., et al. (2017) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid.2017.52 · Cited by 9

Cloud Cyber Security: Finding an Effective Approach with Unikernels

Duncan, B., Happe, A., & Bratterud, A. (2017) · InTech eBooks · doi:10.5772/67801 · Cited by 9

Investigation of Impacts on Network Performance in the Advance of a Microservice Design

Kratzke, N., & Quint, P.-C. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-62594-2_10 · Cited by 9

ABP Scheduler: Speeding Up Service Spread in Docker Swarm

Wu, Y. L., & Chen, H. (2017) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa/iucc.2017.00108 · Cited by 9

Toward Fine-Grained, Privacy-Preserving, Efficient Multi-Domain Network Resource Discovery

Xiang, Q., Zhang, J. J., Wang, X. T., et al. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2927073 · Cited by 9

Mitigating stragglers to avoid QoS violation for time-critical applications through dynamic server blacklisting

Ouyang, X., Wang, C., & Xu, J. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.07.017 · Cited by 9

Effectively Testing System Configurations of Critical IoT Analytics Pipelines

Geldenhuys, M. K., Thamsen, L., Gontarska, K. K., et al. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005504 · Cited by 9

Bullfighting extreme scenarios in efficient hyper-scale cluster computing

Fernández-Cerero, D., Irizo, F. J. O., Fernández-Montes, A., & Morente, F. V. (2020) · Cluster Computing · doi:10.1007/s10586-020-03094-2 · Cited by 9

Scalable build service system with smart scheduling service

Wang, K., Tener, G., Gullapalli, V., et al. (2020) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3395363.3397371 · Cited by 9

CPI for Runtime Performance Measurement: The Good, the Bad, and the Ugly

Li, Y., Li, C., & Guo, J. (2020) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc50251.2020.00019 · Cited by 9

Khaos: Dynamically Optimizing Checkpointing for Dependable Distributed Stream Processing

Geldenhuys, M. K., Pfister, B., Scheinert, D., et al. (2022) · Annals of Computer Science and Information Systems · doi:10.15439/2022f225 · Cited by 9

Scheduling Challenges for Variable Capacity Resources

Zhang, C., & Chien, A. A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88224-2_10 · Cited by 9

QD-Compressor: a Quantization-based Delta Compression Framework for Deep Neural Networks

Zhang, S., Wu, D., Jin, H., et al. (2021) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd53106.2021.00088 · Cited by 9

Dynamic Resource Management for Efficient Utilization of Multitasking GPUs

Park, J. J. K., Park, Y., & Mahlke, S. A. (2017) · ACM SIGOPS Operating Systems Review · doi:10.1145/3093315.3037707 · Cited by 9

Escra: Event-driven, Sub-second Container Resource Allocation

Cusack, G., Nazari, M., Goodarzy, S., Hunhoff, E., Oberai, P., Keller, E. R., Rozner, E., & Han, R. Y. (2022) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs54860.2022.00038 · Cited by 9

Squall

Vitorović, A., Elseidy, M., Guliyev, K., et al. (2016) · Proceedings of the VLDB Endowment · doi:10.14778/3007263.3007307 · Cited by 9

Analysis on network performance of container virtualization on IoT devices

Lee, K., Kim, H., Kim, B., et al. (2017) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc.2017.8190937 · Cited by 9

Orchestrator conversation: Distributed management of cloud applications

Sebrechts, M., Seghbroeck, G. V., Wauters, T., et al. (2018) · International Journal of Network Management · doi:10.1002/nem.2036 · Cited by 9

FUSE: A Microservice Approach to Cross-domain Federation using Docker Containers

Goethals, T., Kerkhove, D., Hoye, L. V., et al. (2019) · International Conference on Cloud Computing and Services Science · doi:10.5220/0007706000900099 · Cited by 9

Predicting Remediations for Hardware Failures in Large-Scale Datacenters

Lin, F., Davoli, A., Akbar, I., et al. (2020) · IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S) · doi:10.1109/dsn-s50200.2020.00016 · 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

The modulation of synoptic weather patterns and human activities on the diurnal cycle of the summertime canopy urban heat island in the Yangtze River Delta Urban Agglomeration, China

Shi, T., Yang, Y., Zong, L., Guo, M., Qi, P., & Lolli, S. (2025) · Atmospheric chemistry and physics · doi:10.5194/acp-25-4989-2025 · Cited by 11

Scalable Video Conferencing Using SDN Principles

Michel, O., Sengupta, S., Kim, H., et al. (2025) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3718958.3750489 · 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

Cluster as a Service: A Container Based Cluster Sharing Approach with Multi-user Support

Cui, W., Zhan, H., Li, B., et al. (2016) · IEEE Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose.2016.16 · Cited by 8

CloudLightning: a Self-Organized Self-Managed Heterogeneous Cloud

Xiong, H., Dong, D., Filelis‐Papadopoulos, C. K., et al. (2017) · Annals of Computer Science and Information Systems · doi:10.15439/2017f274 · Cited by 8

Efficient covering for top-k filtering in content-based publish/subscribe systems

Zhang, K., Sadoghi, M., Muthusamy, V., et al. (2017) · doi:10.1145/3135974.3135976 · Cited by 8

Optimizing Interrupt Handling Performance for Memory Failures in Large Scale Data Centers

Dixit, H. D., Lin, F. (., Holland, B., et al. (2020) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3358960.3375793 · Cited by 8

Fine-Grained, Multi-Domain Network Resource Abstraction as a Fundamental Primitive to Enable High-Performance, Collaborative Data Sciences

Xiang, Q., Zhang, J., Xian-sheng, W. A. N. G., et al. (2018) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc.2018.00008 · Cited by 8

Memtrade: Marketplace for Disaggregated Memory Clouds

Maruf, H. A., Zhong, Y., Wang, H., et al. (2023) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3589985 · Cited by 8

Fast DRL-based scheduler configuration tuning for reducing tail latency in edge-cloud jobs

Wen, S., Han, R., Liu, C. H., & Chen, L. Y. (2023) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-023-00465-z · Cited by 8

Improving Cluster Utilization Through Adaptive Resource Management for Deep Neural Network and CPU Jobs Colocation

Zhao, H., Cui, W., Chen, Q., et al. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3303988 · 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

Evolving the Digital Industrial Infrastructure for Production: Steps Taken and the Road Ahead

Pennekamp, J., Belova, A., Bergs, T., et al. (2023) · Interdisciplinary excellence accelerator series · doi:10.1007/978-3-031-44497-5_2 · Cited by 8

Integrating Kubernetes Autoscaling for Cost Efficiency in Cloud Services

Kavuri, S. (2024) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit241051038 · Cited by 8

Tango: Harmonious Optimization for Mixed Services in Kubernetes-Based Edge Clouds

Shen, S., Feng, Y., Xu, M., et al. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3479926 · Cited by 8

Understanding and improving JVM GC work stealing at the data center scale

Hassanein, W. M. (2016) · ACM SIGPLAN International Symposium on Memory Management (ISMM) · doi:10.1145/2926697.2926706 · Cited by 8

Data processing platforms for electronic health records

Essa, Y. M., Attiya, G., El‐Sayed, A., & Elmahalawy, A. (2018) · Health and Technology · doi:10.1007/s12553-018-0219-5 · Cited by 9

Zeno: A Straggler Diagnosis System for Distributed Computing Using Machine Learning

Shen, H., & Li, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-92040-5_8 · Cited by 9

DC-DRF

Kash, I. A., O’Shea, G., & Volos, S. (2018) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3267809.3267848 · Cited by 8

dCCPI-predictor: A state-aware approach for effectively predicting cross-core performance interference

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