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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Managing Tail Latency in Datacenter-Scale File Systems Under Production Constraints

Misra, P. A., Borge, M. F., Goiri, Í., et al. (2019) · doi:10.1145/3302424.3303973 · Cited by 39

Energy-aware dynamic resource management in elastic cloud datacenters

Khan, A. A., Zakarya, M., & Khan, R. (2018) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2018.12.001 · Cited by 38

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

QoS-Aware Co-Scheduling for Distributed Long-Running Applications on Shared Clusters

Zhu, J., Yang, R., Sun, X., et al. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3202493 · Cited by 37

Vision

Saeed, A., Ammar, M., Harras, K. A., & Zegura, E. W. (2015) · doi:10.1145/2802130.2802133 · Cited by 36

FastBuild: Accelerating Docker Image Building for Efficient Development and Deployment of Container

Huang, Z., Wu, S., Jiang, S., et al. (2019) · Symposium on Mass Storage Systems and Technologies (MSST) · doi:10.1109/msst.2019.00-18 · Cited by 36

VM Live Migration At Scale

Ruprecht, A., Jones, D., Shiraev, D., et al. (2018) · ACM SIGPLAN Notices · doi:10.1145/3296975.3186415 · Cited by 36

High-Performance Modelling and Simulation for Big Data Applications

Kołodziej, J., & González–Vélez, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16272-6 · Cited by 37

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

Computing at Massive Scale: Scalability and Dependability Challenges

Yang, R., & Xu, J. (2016) · IEEE International Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose.2016.73 · Cited by 35

Deploying High Throughput Scientific Workflows on Container Schedulers with Makeflow and Mesos

Zheng, C., Tovar, B., & Thain, D. (2017) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid.2017.9 · Cited by 35

Client-Side Scheduling Based on Application Characterization on Kubernetes

Medel, V., Tolón, C., Arronategui, U., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68066-8_13 · Cited by 35

An Extended Energy-Aware Cost Recovery Approach for Virtual Machine Migration

Zakarya, M. (2018) · IEEE Systems Journal · doi:10.1109/jsyst.2018.2829890 · Cited by 35

GAME-SCORE: Game-based energy-aware cloud scheduler and simulator for computational clouds

Fernández-Cerero, D., Jakóbik, A., Fernández-Montes, A., & Kołodziej, J. (2018) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2018.09.001 · Cited by 35

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

CoLocateMe: Aggregation-Based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds

Zakarya, M., Gillam, L., Salah, K., et al. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2022.3181375 · Cited by 34

Resource management in cloud platform as a service systems: Analysis and opportunities

Costache, S., Dib, D., Parlavantzas, N., et al. (2017) · Journal of Systems and Software · doi:10.1016/j.jss.2017.05.035 · Cited by 34

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

Towards distributed machine learning in shared clusters: A dynamically-partitioned approach

Sun, P., Wen, Y., Nguyen Binh Duong, T. A., et al. (2017) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 34

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

A Kubernetes Autoscaler Based on Pod Replicas Prediction

Hu, T., & Wang, Y. (2021) · Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) · doi:10.1109/acctcs52002.2021.00053 · Cited by 34

Controlled Dynamic Fair Division

Friedman, E., Psomas, A., & Vardi, S. (2017) · ACM Conference on Economics and Computation (EC) · doi:10.1145/3033274.3085123 · Cited by 33

Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment

Lin, F., Muzumdar, K., Laptev, N., Curelea, M.-V., Lee, S., & Sankar, S. (2020) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3392149 · Cited by 33

Monarch

Adams, C., Alonso, L., Atkin, B., Banning, J., Bhola, S., Buskens, R., Chen, M., Chen, X., Chung, Y., Jia, Q., et al. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3181-3194 · Cited by 44

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 35

Implementing Effective SLO Monitoring in High-Volume Data Processing Systems

Kavuri, S., & Narne, S. (2020) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit206479 · Cited by 32

Tempo

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

SLO-aware colocation of data center tasks based on instantaneous processor requirements

Janus, P., & Rzaḑca, K. (2017) · Symposium on Cloud Computing · doi:10.1145/3127479.3132244 · Cited by 32

Model-driven computational sprinting

Morris, N., Stewart, C., Chen, L. Y., et al. (2018) · doi:10.1145/3190508.3190543 · Cited by 32

Container Orchestration With Cost-Efficient Autoscaling in Cloud Computing Environments

Rodriguez, M. A., & Buyya, R. (2020) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-7998-2701-6.ch010 · Cited by 32

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

Cider: a Rapid Docker Container Deployment System through Sharing Network Storage

Du, L., Wo, T., Yang, R., et al. (2017) · 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.2017.44 · Cited by 32

Kubernetes as a Standard Container Orchestrator - A Bibliometric Analysis

Carrión, C. (2022) · Journal of Grid Computing · doi:10.1007/s10723-022-09629-8 · Cited by 44

Serverless Cold Starts and Where to Find Them

Joosen, A., Hassan, A., Asenov, M., et al. (2025) · doi:10.1145/3689031.3696073 · Cited by 31

Consensus in the Cloud: Paxos Systems Demystified

Ailijiang, A., Charapko, A., & Demirbaş, M. (2016) · International Conference on Computer Communication and Networks (ICCCN) · doi:10.1109/icccn.2016.7568499 · Cited by 31

An Energy Aware Cost Recovery Approach for Virtual Machine Migration

Zakarya, M., & Gillam, L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61920-0_13 · Cited by 31

Procella

Chattopadhyay, B., Dutta, P., Liu, W., et al. (2019) · Proceedings of the VLDB Endowment · doi:10.14778/3352063.3352121 · Cited by 31

Turbine: Facebook’s Service Management Platform for Stream Processing

Mei, Y., Cheng, L., Talwar, V., et al. (2020) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde48307.2020.00141 · Cited by 31

RPTCN: Resource Prediction for High-dynamic Workloads in Clouds based on Deep Learning

Chen, W., Lu, C., Ye, K., et al. (2021) · Proceedings · doi:10.1109/cluster48925.2021.00038 · Cited by 31

George

Li, S., Wang, L., Wang, W., Yu, Y., & Li, B. (2021) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3472883.3486971 · Cited by 31

Energy-Efficient Joint Task Assignment and Migration in Data Centers: A Deep Reinforcement Learning Approach

Lou, J., Tang, Z., & Jia, W. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3210204 · Cited by 31

Profiling Hyperscale Big Data Processing

Gonzalez, A., Kolli, A., Khan, S., et al. (2023) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3579371.3589082 · Cited by 30

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

Workload-Adaptive Configuration Tuning for Hierarchical Cloud Schedulers

Han, R., Liu, C. H., Zong, Z., Chen, L. Y., Liu, W., Wang, S., & Zhan, J. (2019) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2019.2923197 · Cited by 30

Privacy-Preserving Energy Sharing Among Cloud Service Providers via Collaborative Job Scheduling

Sun, Y., Ding, Z., Yan, Y., Wang, Z., Dehghanian, P., & Lee, W. (2024) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2024.3482390 · Cited by 31

Improving Resource Efficiency of Container-Instance Clusters on Clouds

Awada, U., & Barker, A. (2017) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid.2017.113 · Cited by 29

Co-residency Attacks on Containers are Real

Shringarputale, S., McDaniel, P. D., Butler, K. R. B., et al. (2020) · doi:10.1145/3411495.3421357 · Cited by 29

Towards Edge Computing as a Service: Dynamic Formation of the Micro Data-Centers

Simić, M., Prokić, I., Dedeić, J., Sladić, G., & Milosavljević, B. (2021) · IEEE Access · doi:10.1109/access.2021.3104475 · Cited by 29

Online Scheduling Algorithm for Heterogeneous Distributed Machine Learning Jobs

Zhou, R., Pang, J., Zhang, Q., et al. (2022) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2022.3143153 · Cited by 29

Transient Guarantees: Maximizing the Value of Idle Cloud Capacity

Shastri, S., Rizk, A., & Irwin, D. (2016) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc.2016.84 · Cited by 29

Configuration and Placement of Serverless Applications Using Statistical Learning

Raza, A., Akhtar, N., Isahagian, V., et al. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3254437 · Cited by 29

DIANNE: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure

Coninck, E. D., Bohez, S., Leroux, S., Verbelen, T., Vankeirsbilck, B., Simoens, P., & Dhoedt, B. (2018) · Journal of Systems and Software · doi:10.1016/j.jss.2018.03.032 · Cited by 29

Energy-Efficient Dynamic Virtual Machine Management in Data Centers

Han, Z., Tan, H., Wang, R., Chen, G., Li, Y., & Lau, F. C. M. (2019) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2019.2891787 · Cited by 29

Optimizing Cloud Performance: A Microservice Scheduling Strategy for Enhanced Fault-Tolerance, Reduced Network Traffic, and Lower Latency

Alelyani, A., Datta, A., & Hassan, G. M. (2024) · IEEE Access · doi:10.1109/access.2024.3373316 · Cited by 28

Autoscaling techniques in cloud-native computing: A comprehensive survey

Jeong, B., Jeong, Y., & Jeong, Y. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100791 · Cited by 28

Picocenter

Zhang, L., Litton, J., Cangialosi, F., Benson, T., Levin, D., & Mislove, A. E. (2016) · European Conference on Computer Systems · doi:10.1145/2901318.2901345 · Cited by 28

Aladdin: Optimized Maximum Flow Management for Shared Production Clusters

Wu, H., Zhang, W., Xu, Y., et al. (2019) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2019.00078 · Cited by 28

An Experience Report of Generating Load Tests Using Log-Recovered Workloads at Varying Granularities of User Behaviour

Chen, J., Shang, W., Hassan, A. E., Wang, Y., & Lin, J. (2019) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2019.00068 · Cited by 28

Improving resource utilization by timely fine-grained scheduling

Jin, T., Cai, Z., Li, B., et al. (2020) · doi:10.1145/3342195.3387551 · Cited by 28

Heterogeneous Task Co-location in Containerized Cloud Computing Environments

Zhong, Z., He, J., Rodriguez, M. A., et al. (2020) · IEEE International Symposium on Real-Time Distributed Computing (ISORC) · doi:10.1109/isorc49007.2020.00021 · Cited by 28

Online scheduling of heterogeneous distributed machine learning jobs

Zhang, Q., Zhou, R., Wu, C., et al. (2020) · ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) · doi:10.1145/3397166.3409128 · Cited by 28

On the Resource Management of Kubernetes

Kim, E., Lee, K., & Yoo, C. (2021) · International Conference on Information Networking · doi:10.1109/icoin50884.2021.9333977 · Cited by 28

SLearn: A Case for Task Sampling Based Learning for Cluster Job Scheduling

Jajoo, A., Hu, Y., Lin, X., & Deng, N. (2022) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2022.3222649 · Cited by 28

rFaaS: Enabling High Performance Serverless with RDMA and Leases

Copik, M., Taranov, K., Calotoiu, A., & Hoefler, T. (2023) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps54959.2023.00094 · Cited by 39

Towards Distributed Machine Learning in Shared Clusters: A Dynamically-Partitioned Approach

Sun, P., Wen, Y., Ta, N. B. D., et al. (2017) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp.2017.7947053 · Cited by 28

A Performance Comparison of Containers and Virtual Machines in Workload Migration Context

Tay, Y. C., Gaurav, K., & Karkun, P. (2017) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw.2017.44 · Cited by 28

Energy-aware scheduling for spark job based on deep reinforcement learning in cloud

Li, H., Lu, L., Shi, W., Tan, G., & Luo, H. (2023) · Computing · doi:10.1007/s00607-023-01171-z · Cited by 27

BigLake: BigQuery's Evolution toward a Multi-Cloud Lakehouse

Levandoski, J. J., Casto, G., Deng, M., et al. (2024) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3626246.3653388 · Cited by 27

Leveraging application classes to save power in highly-utilized data centers

Kaffes, K., Sbîrlea, D., Lin, Y., et al. (2020) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3419111.3421274 · Cited by 27

Characterizing Job Microarchitectural Profiles at Scale: Dataset and Analysis

Wang, K., Li, Y., Wang, C., et al. (2022) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3545008.3545026 · Cited by 27

Koordinator: A Service Approach for Replicating Docker Containers in Kubernetes

Netto, H. V., Luiz, A. F., Correia, M. P., et al. (2018) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc.2018.8538452 · Cited by 27

Secrets Revealed in Container Images: An Internet-wide Study on Occurrence and Impact

Dahlmanns, M., Sander, C., Decker, R., et al. (2023) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3579856.3590329 · Cited by 26

Revisiting Reliability in Large-Scale Machine Learning Research Clusters

Kokolis, A., Kuchnik, M., Hoffman, J., Kumar, A., Malani, P., Ma, F., DeVito, Z., Sengupta, S., Saladi, K., & Wu, C.-J. (2025) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca61900.2025.00096 · Cited by 74

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

Wang, C., Urgaonkar, B., Gupta, A., et al. (2016) · IEEE International Conference on Autonomic Computing (ICAC) · doi:10.1109/icac.2016.10 · Cited by 26

Elasecutor

Liu, L., & Xu, H. L. (2018) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3267809.3267818 · Cited by 26

Data Center Network Design for Internet-Related Services and Cloud Computing

Liang, Y., Lu, M., Shen, Z. M., et al. (2019) · Production and Operations Management · doi:10.1111/poms.13355 · Cited by 26

Self-Tuning Query Scheduling for Analytical Workloads

Wagner, B., Kohn, A., & Neumann, T. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3457260 · Cited by 26

ServerMore

Suresh, A., & Gandhi, A. (2021) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3472883.3486979 · Cited by 25

Redy

Zhang, Q., Bernstein, P. A., Berger, D. S., et al. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3503585.3503587 · Cited by 26

LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems

Sabek, I., Ukyab, T. S., & Kraska, T. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3526158 · Cited by 26

Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and Opportunities

Narasayya, V. R., & Chaudhuri, S. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3522566 · Cited by 26

Page Fault Support for Network Controllers

Lesokhin, I., Eran, H., Raindel, S., Shapiro, G., Grimberg, S., Liss, L., Ben-Yehuda, M., Amit, N., & Tsafrir, D. (2017) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3037697.3037710 · Cited by 30

Resource Deflation

Sharma, P., Ali-Eldin, A., & Shenoy, P. J. (2019) · European Conference on Computer Systems · doi:10.1145/3302424.3303945 · Cited by 26

Memory Disaggregation: Research Problems and Opportunities

Liu, L., Cao, W., Şahin, S., Zhang, Q., Bae, J., & Wu, Y. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00165 · Cited by 26

FLEDGE: Kubernetes Compatible Container Orchestration on Low-Resource Edge Devices

Goethals, T., Turck, F. D., & Volckaert, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-38651-1_16 · Cited by 26

Limiting Global Warming by Improving Data-Centre Software

Fernández-Cerero, D., Fernández-Montes, A., & Jakóbik, A. (2020) · IEEE Access · doi:10.1109/access.2020.2978306 · Cited by 26

Acto: automatic end-to-end testing for operation correctness of cloud system management

Gu, J. T., Sun, X., Zhang, W., Jiang, Y., Wang, C., Vaziri, M., Legunsen, O., & Xu, T. (2023) · Proceedings of the 29th Symposium on Operating Systems Principles · doi:10.1145/3600006.3613161 · Cited by 22

Increasing large-scale data center capacity by statistical power control

Wang, G., Wang, S., Luo, B., et al. (2016) · European Conference on Computer Systems · doi:10.1145/2901318.2901338 · Cited by 25

Towards Lightweight Serverless Computing via Unikernel as a Function

Tan, B., Liu, H., Rao, J., et al. (2020) · IEEE/ACM International Symposium on Quality of Service (IWQoS) · doi:10.1109/iwqos49365.2020.9213020 · Cited by 25

CPU Scheduling in Data Centers Using Asynchronous Finite-Time Distributed Coordination Mechanisms

Grammenos, A., Charalambous, T., & Kalyvianaki, E. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3236214 · Cited by 28

Resource Demand Misalignment: An Important Factor to Consider for Reducing Resource Over-Provisioning in Cloud Datacenters

Shen, H., & Chen, L. (2018) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2018.2823642 · Cited by 25

Multi-resource fair sharing for datacenter jobs with placement constraints

Wang, W., Li, B., Liang, B., & Li, J. (2016) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.5555/3014904.3015020 · Cited by 24

GENPACK: A Generational Scheduler for Cloud Data Centers

Havet, A., Schiavoni, V., Felber, P. A., et al. (2017) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e.2017.15 · Cited by 24

Enhancing cloud security and privacy: Time for a new approach?

Duncan, B., Bratterud, A., & Happe, A. (2016) · International Conference on Innovative Computing Technology · doi:10.1109/intech.2016.7845113 · Cited by 24

Elastic management of cloud applications using adaptive reinforcement learning

Lolos, K., Konstantinou, I., Kantere, V., et al. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8257928 · Cited by 24

EnergyQARE

Chen, H., Zhang, Y., Caramanis, M. C., & Coskun, A. K. (2019) · ACM Transactions on Modeling and Performance Evaluation of Computing Systems · doi:10.1145/3243172 · Cited by 24

An Efficient and Non-Intrusive GPU Scheduling Framework for Deep Learning Training Systems

Wang, S., Gonzalez, O. J., Zhou, X., Williams, T., Friedman, B. D., Havemann, M., & Woo, T. L. (2020) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41405.2020.00094 · Cited by 25

CAMIG: Concurrency-Aware Live Migration Management of Multiple Virtual Machines in SDN-Enabled Clouds

He, T., Toosi, A. N., & Buyya, R. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3139014 · Cited by 24

Re-evaluating Measurement Algorithms in Software

Alipourfard, O., Moshref, M., & Yu, M. (2015) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/2834050.2834064 · Cited by 24

Efficient and Deterministic Scheduling for Parallel State Machine Replication

Mendizabal, O. M., Moura, R. S. T. D., Dotti, F. L., et al. (2017) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2017.29 · Cited by 24

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