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

Overcommitment in Cloud Services: Bin Packing with Chance Constraints

Cohen, M. C., Keller, P. W., Mirrokni, V., & Zadimoghaddam, M. (2019) · Management Science · doi:10.1287/mnsc.2018.3091 · Cited by 70

Energy, performance and cost efficient datacenters: A survey

Zakarya, M. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.06.005 · Cited by 69

SHOWAR

Baarzi, A. F., & Kesidis, G. (2021) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3472883.3486999 · Cited by 68

IoTEF: A Federated Edge-Cloud Architecture for Fault-Tolerant IoT Applications

Javed, A., Robert, J., Heljanko, K., & Främling, K. (2020) · Journal of Grid Computing · doi:10.1007/s10723-019-09498-8 · Cited by 67

M edea

Garefalakis, P., Karanasos, K., Pietzuch, P. R., et al. (2018) · doi:10.1145/3190508.3190549 · Cited by 66

Park: An Open Platform for Learning-Augmented Computer Systems

Mao, H., Negi, P., Narayan, A., Wang, H., Yang, J., Wang, H., Marcus, R., Addanki, R., Shirkoohi, M. K., He, S., et al. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 67

Prefetched Address Translation

Margaritov, A., Ustiugov, D., Bugnion, E., et al. (2019) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3352460.3358294 · Cited by 66

A Survey on Scheduling Techniques in Computing and Network Convergence

Tang, S., Yu, Y., Wang, H., Wang, G., Chen, W., Xu, Z., Guo, S., & Gao, W. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3329027 · Cited by 77

An Energy and Performance Aware Consolidation Technique for Containerized Datacenters

Khan, A. A., Zakarya, M., Buyya, R., Khan, R., Khan, M., & RANA, O. F. (2019) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2019.2920914 · Cited by 65

A Blockchain-Based Containerized Edge Computing Platform for the Internet of Vehicles

Cui, L., Chen, Z., Yang, S., Ming, Z., Li, Q. L., Zhou, Y., Chen, S., & Lu, Q. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3027700 · Cited by 64

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

Extending Kubernetes Clusters to Low-Resource Edge Devices Using Virtual Kubelets

Goethals, T., Turck, F. D., & Volckaert, B. (2020) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2020.3033807 · Cited by 63

Jupiter rising

Singh, A., Ong, J. S., Agarwal, A., et al. (2016) · Communications of the ACM · doi:10.1145/2975159 · Cited by 62

Take it to the limit

Bashir, N., Deng, N., Rzaḑca, K., et al. (2021) · doi:10.1145/3447786.3456259 · Cited by 62

Data-driven flexibility assessment for internet data center towards periodic batch workloads

Cao, Y., Cheng, M., Zhang, S., Mao, H., Wang, P., Li, C., Feng, Y., & Ding, Z. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119665 · Cited by 64

Lyra: Elastic Scheduling for Deep Learning Clusters

Li, J., Xu, H. L., Zhu, Y., Liu, Z., Guo, C., & Wang, C. (2023) · European Conference on Computer Systems · doi:10.1145/3552326.3587445 · Cited by 78

Open problems in queueing theory inspired by datacenter computing

Harchol‐Balter, M. (2021) · Queueing Systems · doi:10.1007/s11134-020-09684-6 · Cited by 62

PerfOrator

Rajan, K., Kakadia, D., Curino, C., et al. (2016) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/2987550.2987566 · Cited by 61

The IX Operating System

Belay, A., Prekas, G., Primorac, M., et al. (2016) · ACM Transactions on Computer Systems · doi:10.1145/2997641 · Cited by 61

SCORE: Simulator for cloud optimization of resources and energy consumption

Fernández-Cerero, D., Fernández-Montes, A., Jakóbik, A., et al. (2018) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2018.01.004 · Cited by 61

Characterizing Co-located Datacenter Workloads

Cheng, Y., Chai, Z., & Anwar, A. (2018) · Asia-Pacific Workshop on Systems · doi:10.1145/3265723.3265742 · Cited by 61

Profiling a warehouse-scale computer

Kanev, S., Darago, J. P., Hazelwood, K., et al. (2015) · ACM SIGARCH Computer Architecture News · doi:10.1145/2872887.2750392 · Cited by 61

How Does It Function? Characterizing Long-term Trends in Production Serverless Workloads

Joosen, A., Hassan, A., Asenov, M., Singh, R., Darlow, L. N., Wang, J., & Barker, A. (2023) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3620678.3624783 · Cited by 64

An Availability Analysis Approach for Deployment Configurations of Containers

Sebastio, S., Ghosh, R., & Mukherjee, T. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2017.2788442 · Cited by 60

KubeShare: A Framework to Manage GPUs as First-Class and Shared Resources in Container Cloud

Yeh, T.-A., Chen, H., & Chou, J. (2020) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/3369583.3392679 · Cited by 67

Paxos vs Raft

Howard, H. C., & Mortier, R. (2020) · Workshop on Principles and Practice of Consistency for Distributed Data (PaPoC) · doi:10.1145/3380787.3393681 · Cited by 61

PigPaxos: Devouring the Communication Bottlenecks in Distributed Consensus

Charapko, A., Ailijiang, A., & Demirbaş, M. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3452834 · Cited by 58

ViperProbe: Rethinking Microservice Observability with eBPF

Levin, J., & Benson, T. (2020) · IEEE International Conference on Cloud Networking · doi:10.1109/cloudnet51028.2020.9335808 · Cited by 58

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

Hermod

Kaffes, K., Yadwadkar, N. J., & Kozyrakis, C. (2022) · Symposium on Cloud Computing · doi:10.1145/3542929.3563468 · Cited by 73

Accelerated Service Chaining on a Single Switch ASIC

Wu, D., Chen, A., Ng, T. S. E., Wang, G., & Wang, H. (2019) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/3365609.3365849 · Cited by 58

Data Center Power Oversubscription with a Medium Voltage Power Plane and Priority-Aware Capping

Sakalkar, V., Kontorinis, V., Landhuis, D. P., et al. (2020) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3373376.3378533 · Cited by 57

A Comprehensive Feature Comparison Study of Open-Source Container Orchestration Frameworks

Truyen, E., Landuyt, D. V., Preuveneers, D., Lagaisse, B., & Joosen, W. (2019) · Applied Sciences · doi:10.3390/app9050931 · Cited by 65

Preemptive, low latency datacenter scheduling via lightweight virtualization

Chen, W., Rao, J., & Zhou, X. (2017) · USENIX Annual Technical Conference · Cited by 56

Efficient Container Assignment and Layer Sequencing in Edge Computing

Lou, J., Luo, H., Tang, Z., Jia, W., & Zhao, W. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2022.3159728 · Cited by 56

How Does the Workload Look Like in Production Cloud? Analysis and Clustering of Workloads on Alibaba Cluster Trace

Chen, W., Ye, K., Wang, Y., et al. (2018) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/padsw.2018.8644579 · Cited by 56

Energy, performance and cost efficient cloud datacentres: A survey

Khan, A. A., & Zakarya, M. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100390 · Cited by 55

Tiansuan Constellation: An Open Research Platform

Wang, S., Li, Q., Xu, M., Ma, X., Zhou, A., & Sun, Q. (2021) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge53862.2021.00022 · Cited by 55

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

SmartHarvest

Wang, Y., Arya, K., Kogias, M., Vanga, M., Bhandari, A., Yadwadkar, N. J., Sen, S., Elnikety, S., Kozyrakis, C., & Bianchini, R. (2021) · European Conference on Computer Systems · doi:10.1145/3447786.3456225 · Cited by 55

Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework

Deshmukh, S., Rao, K. T., & Shabaz, M. (2021) · Security and Communication Networks · doi:10.1155/2021/8340925 · Cited by 54

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

Capacity planning for IaaS cloud providers offering multiple service classes

Carvalho, M., Menascé, D. A., & Brasileiro, F. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.07.019 · Cited by 53

Intelligent time-series forecasting framework for non-linear dynamic workload and resource prediction in cloud

Ullah, F., Bilal, M., & Yoon, S.-K. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.109653 · Cited by 55

Elastic Provisioning of Virtual Machines for Container Deployment

Nardelli, M., Hochreiner, C., & Schulte, S. (2017) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3053600.3053602 · Cited by 53

Scavenger

Javadi, S. A., Suresh, A., Wajahat, M., et al. (2019) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3357223.3362734 · Cited by 53

Designing Cloud Servers for Lower Carbon

Wang, J., Berger, D. S., Kazhamiaka, F., Irvene, C., Zhang, C., Choukse, E., Frost, K., Fonseca, R., Warrier, B., Bansal, C., et al. (2024) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca59077.2024.00041 · Cited by 52

A Taxonomy of Job Scheduling on Distributed Computing Systems

Lopes, R., & Menascé, D. A. (2016) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2016.2537821 · Cited by 52

ClouNS - a Cloud-Native Application Reference Model for Enterprise Architects

Kratzke, N., & Peinl, R. (2016) · IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW) · doi:10.1109/edocw.2016.7584353 · Cited by 52

Computation Reuse in Analytics Job Service at Microsoft

Jindal, A., Qiao, S., Patel, H. B., et al. (2018) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3183713.3190656 · Cited by 52

Containers for Virtualization: An Overview

da Silva, V. G., Kirikova, M., & Alksnis, G. (2018) · Applied Computer Systems · doi:10.2478/acss-2018-0003 · Cited by 51

Emergent Failures: Rethinking Cloud Reliability at Scale

Garraghan, P., Yang, R., Wen, Z., Romanovsky, A. B., Xu, J., Buyya, R., & Ranjan, R. K. (2018) · IEEE Cloud Computing · doi:10.1109/mcc.2018.053711662 · Cited by 51

Stable and efficient resource management using deep neural network on cloud computing

Jeong, B., Baek, S., Park, S., et al. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.089 · Cited by 50

A survey and taxonomy on workload scheduling and resource provisioning in hybrid clouds

Wang, B., Wang, C., Song, Y., Cao, J., Cui, X., & Zhang, L. (2020) · Cluster Computing · doi:10.1007/s10586-020-03048-8 · Cited by 50

Multi-resource Fair Sharing for Datacenter Jobs with Placement Constraints

Wang, W., Li, B., Liang, B., et al. (2016) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc.2016.85 · Cited by 50

Pado

Yang, Y., Kim, G.-W., Song, W. W., et al. (2017) · European Conference on Computer Systems · doi:10.1145/3064176.3064181 · Cited by 49

Towards a full-stack devops environment (platform-as-a-service) for cloud-hosted applications

Li, Z., Zhang, Y., & Liu, Y. (2017) · Tsinghua Science & Technology · doi:10.1109/tst.2017.7830891 · Cited by 48

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

Metis: Learning to Schedule Long-Running Applications in Shared Container Clusters at Scale

Wang, L., Weng, Q., Wang, W., Chen, C., & Li, B. (2020) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41405.2020.00072 · Cited by 48

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

Stochastic Resource Provisioning for Containerized Multi-Tier Web Services in Clouds

Adam, O., Lee, Y. C., & Zomaya, A. Y. (2016) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2016.2639009 · Cited by 46

Elastic Stream Processing for the Internet of Things

Hochreiner, C., Vögler, M., Schulte, S., et al. (2016) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2016.0023 · Cited by 46

SSD failures in the field

Alter, J., Xue, J., Dimnaku, A., et al. (2019) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3295500.3356172 · Cited by 46

Adaptive Function Launching Acceleration in Serverless Computing Platforms

Xu, Z., Zhang, H., Geng, X., Wu, Q., & Ma, H. (2019) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads47876.2019.00011 · Cited by 52

The evolution of distributed computing systems: from fundamental to new frontiers

Lindsay, D., Gill, S. S., Smirnova, D., & Garraghan, P. (2021) · Computing · doi:10.1007/s00607-020-00900-y · Cited by 45

Elastic Deep Learning in Multi-Tenant GPU Clusters

Wu, Y., Ma, K., Yan, X., Zhi, L., Cai, Z., Huang, Y., Cheng, J. S.-C., Yuan, H., & Yu, F. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3064966 · Cited by 51

A survey of domains in workflow scheduling in computing infrastructures: Community and keyword analysis, emerging trends, and taxonomies

Versluis, L., & Iosup, A. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.04.009 · Cited by 49

Large-Scale Machine Learning Cluster Scheduling via Multi-Agent Graph Reinforcement Learning

Zhao, X., & Wu, C. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3139607 · Cited by 44

Startup-aware Dependent Task Scheduling with Bandwidth Constraints in Edge Computing

Lou, J., Tang, Z., Weijia Jia, B., Zhao, W., & Li, J. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3238868 · Cited by 45

A Taxonomy of Live Migration Management in Cloud Computing

He, T., & Buyya, R. (2024) · Minerva Access (University of Melbourne) · doi:10.1145/3615353 · Cited by 44

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

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

Intelligent Resource Scheduling at Scale: A Machine Learning Perspective

Yang, R., Ouyang, X., Chen, Y., et al. (2018) · IEEE Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose.2018.00025 · Cited by 44

Evolving Container to Unikernel for Edge Computing and Applications in Process Industry

Chen, S., & Zhou, M. (2021) · Processes · doi:10.3390/pr9020351 · Cited by 43

Cloud failure prediction based on traditional machine learning and deep learning

Asmawi, T. N. T., Ismail, A. B., & Shen, J. (2022) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-022-00327-0 · Cited by 60

VM Live Migration At Scale

Ruprecht, A., Jones, D., Shiraev, D., et al. (2018) · ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE) · doi:10.1145/3186411.3186415 · Cited by 43

Warehouse-scale video acceleration: co-design and deployment in the wild

Ranganathan, P., Stodolsky, D., Calow, J., Dorfman, J., Guevara, M., Smullen, C. W., IV, Kuusela, A., Balasubramanian, R., Bhatia, S., Chauhan, P., et al. (2021) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3445814.3446723 · Cited by 43

Watermarks in stream processing systems: Semantics and comparative analysis of Apache Flink and Google Cloud Dataflow

Akidau, T., Begoli, E., Chernyak, S., Hueske, F., Knight, K., Knowles, K., Mills, D., & Sotolongo, D. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3476311.3476389 · Cited by 56

Multi-tenant GPU Clusters for Deep Learning Workloads: Analysis and Implications

Jeon, M., Venkataraman, S., Phanishayee, A., et al. (2018) · Cited by 42

Phoenix: A Constraint-Aware Scheduler for Heterogeneous Datacenters

Thinakaran, P., Gunasekaran, J. R., Sharma, B., et al. (2017) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.262 · Cited by 42

Hydra: a federated resource manager for data-center scale analytics

Curino, C., Krishnan, S., Karanasos, K., et al. (2019) · Networked Systems Design and Implementation · Cited by 42

Job scheduling for large-scale machine learning clusters

Wang, H., Liu, Z., & Shen, H. (2020) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/3386367.3432588 · Cited by 42

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

Archipelago: A Scalable Low-Latency Serverless PlatformPreprint

Singhvi, A., Houck, K., Balasubramanian, A., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.09849 · Cited by 42

COPA: A Combined Autoscaling Method for Kubernetes

Ding, Z., & Huang, Q. (2021) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws53863.2021.00061 · Cited by 41

The MIT Supercloud Dataset

Samsi, S., Weiss, M. L., Bestor, D., Li, B., Jones, M. P., Reuther, A., Edelman, D., Arcand, W., Byun, C., Holodnak, J. T., et al. (2021) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec49654.2021.9622850 · Cited by 41

Memory-harvesting VMs in cloud platforms

Fuerst, A., Novaković, S., Goiri, Í., et al. (2022) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3503222.3507725 · Cited by 41

Topology-Aware Scheduling Framework for Microservice Applications in Cloud

Li, X., Zhou, J., Wei, X., Li, D., Qian, Z., Wu, J., Qin, X., & Lu, S. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3238751 · Cited by 41

DieHard: Reliable Scheduling to Survive Correlated Failures in Cloud Data Centers

Sedaghat, M., Wadbro, E., Wilkes, J., et al. (2016) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid.2016.11 · Cited by 41

Portfolio-driven Resource Management for Transient Cloud Servers

Sharma, P., Irwin, D., & Shenoy, P. J. (2017) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/3078505.3078511 · Cited by 41

Workload characterization of interactive cloud services on big and small server platforms

Chen, S., Gal-On, S., Delimitrou, C., Manne, S., & Martínez, J. F. (2017) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc.2017.8167770 · Cited by 41

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

Power Modeling for Effective Datacenter Planning and Compute Management

Radovanovic, A., Chen, B., Talukdar, S., Roy, B., Duarte, A., & Shahbazi, M. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3125275 · Cited by 43

Cloud Computing Value Chains: Research from the Operations Management Perspective

Chen, S., Moinzadeh, K., Song, J.-S. J., & Zhong, Y. (2020) · Manufacturing & Service Operations Management · doi:10.1287/msom.2022.1178 · Cited by 42

Effective Modeling Approach for IaaS Data Center Performance Analysis under Heterogeneous Workload

Chang, X., Xia, R., Muppala, J. K., et al. (2016) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2016.2560158 · Cited by 40

Towards a container-based architecture for multi-tenant SaaS applications

Truyen, E., Landuyt, D. V., Reniers, V., et al. (2016) · doi:10.1145/3008167.3008173 · Cited by 40

Analyzing Alibaba’s Co-located Datacenter Workloads

Cheng, Y., Anwar, A., & Duan, X. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622518 · Cited by 40

Design of a Quantization-Based DNN Delta Compression Framework for Model Snapshots and Federated Learning

Jin, H., Wu, D., Zhang, S., Zou, X., Jin, S., Tao, D., Liao, Q., & Xia, W. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3230840 · Cited by 42

Towards resource-efficient reactive and proactive auto-scaling for microservice architectures

Ahmad, H., Treude, C., Wagner, M., et al. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112390 · Cited by 39

Value of information based scheduling of cloud computing resources

Bölöni, L. L., & Turgut, D. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2016.10.024 · Cited by 39

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