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

Measuring data-centre workflows complexity through process mining: the Google cluster case

Fernández-Cerero, D., Varela‐Vaca, Á. J., Fernández-Montes, A., et al. (2019) · The Journal of Supercomputing · doi:10.1007/s11227-019-02996-2 · Cited by 24

A Case Against CXL Memory Pooling

Levis, P. A., Lin, K., & Tai, A. (2023) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/3626111.3628195 · Cited by 23

Scheduling Workflow Tasks With Unknown Task Execution Time by Combining Machine-Learning and Greedy-Optimization

Yang, Y., Shen, H., & Tian, H. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3351622 · Cited by 23

Energy and carbon-aware distributed machine learning tasks scheduling scheme for the multi-renewable energy-based edge-cloud continuum

Miao, Z., Liu, L., Nan, H., et al. (2024) · Science and Technology for Energy Transition · doi:10.2516/stet/2024076 · Cited by 23

PerficientCloudSim: a tool to simulate large-scale computation in heterogeneous clouds

Zakarya, M., Gillam, L., Khan, A. A., et al. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-020-03425-5 · Cited by 23

Bypassing the load balancer without regrets

Kogias, M., Iyer, R., & Bugnion, E. (2020) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3419111.3421304 · Cited by 23

The missing pieces of open design enablement

Ansell, T., & Saligane, M. (2020) · doi:10.1145/3400302.3415736 · Cited by 23

Redesigning Data Centers for Renewable Energy

Agarwal, A., Sun, J., Noghabi, S. A., Iyengar, S., Badam, A., Chandra, R., Seshan, S., & Shivkumar Kalyanaraman, A. (2021) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/3484266.3487394 · Cited by 23

FAITH: A Fast Blockchain-Assisted Edge Computing Platform for Healthcare Applications

Ming, Z., Zhou, M., Cui, L., & Yang, S. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3166813 · Cited by 23

Affinity-aware resource provisioning for long-running applications in shared clusters

Mommessin, C., Yang, R., Shakhlevich, N. V., et al. (2023) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2023.02.011 · Cited by 23

faas‐sim : A trace‐driven simulation framework for serverless edge computing platforms

Raith, P., Rausch, T., Furutanpey, A., et al. (2023) · Software Practice and Experience · doi:10.1002/spe.3277 · Cited by 23

Reliable Computing Service in Massive-Scale Systems through Rapid Low-Cost Failover

Yang, R., Zhang, Y., Garraghan, P., et al. (2016) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2016.2544313 · Cited by 23

A Reference Architecture for Datacenter Scheduling: Design, Validation, and Experiments

Andreadis, G., Versluis, L., Mastenbroek, F., et al. (2018) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc.2018.00040 · Cited by 23

TestSage: Regression Test Selection for Large-Scale Web Service Testing

Zhong, H., Zhang, L., & Sarfraz Khurshid, D. (2019) · doi:10.1109/icst.2019.00052 · Cited by 23

Structured Model Pruning of Convolutional Networks on Tensor Processing Units

Chen, K., Franko, K., & Sang, R. (2021) · arXiv · doi:10.48550/arxiv.2107.04191 · Cited by 22

Incentivizing self-capping to increase cloud utilization

Shahrad, M., Klein, C., Zheng, L., et al. (2017) · Symposium on Cloud Computing · doi:10.1145/3127479.3128611 · Cited by 22

DRLPart

Chen, R., Wu, J., Shi, H., et al. (2021) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/3431379.3460648 · Cited by 22

Strong Temporal Isolation Among Containers in OpenStack for NFV Services

Cucinotta, T., Abeni, L., Marinoni, M., Mancini, R., & Vitucci, C. (2021) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2021.3116183 · Cited by 22

Gear: Enable Efficient Container Storage and Deployment with a New Image Format

Fan, H., Bian, S., Wu, S., et al. (2021) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs51616.2021.00020 · Cited by 22

pHPA: A Proactive Autoscaling Framework for Microservice Chain

Choi, B., Park, J., Lee, C., & Han, D. (2021) · Asia-Pacific Workshop on Networking (APNet) · doi:10.1145/3469393.3469401 · Cited by 22

Machine Learning for Computer Systems and Networking: A Survey

Kanakis, M. E., Khalili, R., & Wang, L. (2022) · ACM Computing Surveys · doi:10.1145/3523057 · Cited by 29

Pangea: An MLOps Tool for Automatically Generating Infrastructure and Deploying Analytic Pipelines in Edge, Fog and Cloud Layers

Miñón, R., Díaz-de-Arcaya, J., Torre-Bastida, A. I., et al. (2022) · Sensors · doi:10.3390/s22124425 · Cited by 22

Machine Learning Feature Based Job Scheduling for Distributed Machine Learning Clusters

Wang, H., Liu, Z., & Shen, H. (2022) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2022.3190797 · Cited by 24

SIMPPO

Qiu, H., Mao, W., Patke, A., Wang, C., Franke, H., Kalbarczyk, Z., Başar, T., & Iyer, R. K. (2022) · Symposium on Cloud Computing · doi:10.1145/3542929.3563475 · Cited by 23

Myths and Misconceptions Around Reducing Carbon Embedded in Cloud Platforms

Lyu, J.-X., Wang, J., Frost, K., et al. (2023) · doi:10.1145/3604930.3605717 · Cited by 22

Selecting resources for distributed dataflow systems according to runtime targets

Thamsen, L., Verbitskiy, I., Schmidt, F., et al. (2016) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/pccc.2016.7820629 · Cited by 22

An Auto-Scaling Framework for Containerized Elastic Applications

Tian, Y., Guangtao, X., Shiyou, Q., et al. (2017) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom.2017.40 · Cited by 22

Time Estimation and Resource Minimization Scheme for Apache Spark and Hadoop Big Data Systems With Failures

Lee, J., Kim, B., & Chung, J. (2019) · IEEE Access · doi:10.1109/access.2019.2891001 · Cited by 22

Online evolutionary batch size orchestration for scheduling deep learning workloads in GPU clusters

Bian, Z., Li, S., Wang, W., & You, Y. (2021) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3458817.3480859 · Cited by 23

ComboFunc: Joint Resource Combination and Container Placement for Serverless Function Scaling With Heterogeneous Container

Wen, Z., Chen, Q., Deng, Q., Niu, Y., Song, Z., & Liu, F. (2024) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2024.3454071 · Cited by 21

Dependable IoT Data Stream Processing for Monitoring and Control of Urban Infrastructures

Geldenhuys, M. K., Will, J., Pfister, B. J. J., et al. (2021) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e52221.2021.00041 · Cited by 21

Morrigan: A Composite Instruction TLB Prefetcher

Vavouliotis, G., Alvarez, L., Grot, B., et al. (2021) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3466752.3480049 · Cited by 21

OneEdge

Saurez, E., Gupta, H., Daglis, A., & Ramachandran, U. (2021) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3472883.3487008 · Cited by 21

Workload consolidation in alibaba clusters

Zhang, Y., Yu, Y., Wang, W., et al. (2022) · Symposium on Cloud Computing · doi:10.1145/3542929.3563465 · Cited by 21

SFS: Smart OS Scheduling for Serverless Functions

Fu, Y., Liu, L., Wang, H., Cheng, Y., & Chen, S. (2022) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41404.2022.00047 · Cited by 21

Is Machine Learning Necessary for Cloud Resource Usage Forecasting?

Christofidi, G., Papaioannou, K., & Doudali, T. D. (2023) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3620678.3624790 · Cited by 21

Towards Data-Driven Autonomics in Data Centers

Sîrbu, A., & Babaoğlu, Ö. (2015) · International Conference on Cloud and Autonomic Computing (ICCAC) · doi:10.1109/iccac.2015.19 · Cited by 21

Quokka: Latency-Aware Middlebox Scheduling with dynamic resource allocation

Li, Q., Jiang, Y., Duan, P., et al. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.10.021 · Cited by 21

RDMC: A Reliable RDMA Multicast for Large Objects

Behrens, J., Jha, S., Birman, K., et al. (2018) · IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) · doi:10.1109/dsn.2018.00020 · Cited by 21

ConfAdvisor: A Performance-centric Configuration Tuning Framework for Containers on Kubernetes

Chiba, T., Nakazawa, R., Horii, H., et al. (2019) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e.2019.00031 · Cited by 21

Understanding the Workload Characteristics in Alibaba: A View from Directed Acyclic Graph Analysis

Lu, C., Chen, W., Ye, K., et al. (2020) · International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS) · doi:10.1109/hpbdis49115.2020.9130578 · Cited by 21

Scheduling Methods to Reduce Response Latency of Function as a Service

Żuk, P., & Rzaḑca, K. (2020) · IEEE International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) · doi:10.1109/sbac-pad49847.2020.00028 · Cited by 21

Scheduler technologies in support of high performance data analysis

Reuther, A., Byun, C., Arcand, W., et al. (2016) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec.2016.7761604 · Cited by 20

CEDULE+: Resource Management for Burstable Cloud Instances Using Predictive Analytics

Pinciroli, R., Ali, A. Z. E., Yan, F., et al. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3039942 · Cited by 20

Shard Manager

Lee, S., Guo, Z., Sunercan, O., Jun, Y., Kooburat, T., Biswal, S., Chen, J., Huang, K., Cheung, Y., Zhou, Y., et al. (2021) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3477132.3483546 · Cited by 20

RLQ: Workload Allocation With Reinforcement Learning in Distributed Queues

Staffolani, A., Darvariu, V.-A., Bellavista, P., & Musolesi, M. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3231981 · Cited by 20

Fault Injection and Detection for Artificial Intelligence Applications in Container-Based Clouds

Ye, K., Liu, Y., Xu, G., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-94295-7_8 · Cited by 20

Preemptive and Low Latency Datacenter Scheduling via Lightweight Containers

Chen, W., Zhou, X., & Rao, J. (2019) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2019.2957754 · Cited by 20

Prediction-Based Admission Control for IaaS Clouds with Multiple Service Classes

Carvalho, M., Menascé, D. A., & Brasileiro, F. (2015) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom.2015.16 · Cited by 19

Failure prediction of tasks in the cloud at an earlier stage: a solution based on domain information mining

Liu, C., Dai, L., Lai, Y., et al. (2020) · Computing · doi:10.1007/s00607-020-00800-1 · Cited by 19

LIBRA: An Economical Hybrid Approach for Cloud Applications with Strict SLAs

Raza, A., Zhang, Z., Akhtar, N., et al. (2021) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e52221.2021.00028 · Cited by 19

Repttack: Exploiting Cloud Schedulers to Guide Co-Location Attacks

Fang, C., Wang, H., Nazari, N., et al. (2022) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2022.23149 · Cited by 19

RAS

Newell, A., Skarlatos, D., Fan, J., Kumar, P., Khutornenko, M., Pundir, M., Zhang, Y., Zhang, M., Liu, Y., Le, L., et al. (2021) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3477132.3483578 · Cited by 19

Deep Reinforcement Learning Enhanced Greedy Algorithm for Online Scheduling of Batched Tasks in Cloud in Cloud HPC Systems

Yang, Y., & Shen, H. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3138459 · Cited by 19

Solving the Batch Stochastic Bin Packing Problem in Cloud

Yan, J., Lu, Y., Chen, L., Qin, S., Fang, Y., Lin, Q., Moscibroda, T., Rajmohan, S., & Zhang, D. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539334 · Cited by 19

Going through the Life Cycle of Faults in Clouds: Guidelines on Fault Handling

Li, X., Yu, G., Chen, P., Chen, H., & Chen, Z. (2022) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre55969.2022.00022 · Cited by 24

Intelligent Resource Scaling for Container-Based Digital Twin Simulation of Consumer Electronics

Jeon, J., Jeong, B., & Jeong, Y. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3320174 · Cited by 19

Toward a Standard Interface for Cloud Providers: The Container as the Narrow Waist

Fu, S. D., Liu, J., Chu, X., et al. (2016) · IEEE Internet Computing · doi:10.1109/mic.2016.25 · Cited by 19

Adaptive State Space Partitioning of Markov Decision Processes for Elastic Resource Management

Lolos, K., Konstantinou, I., Kantere, V., et al. (2017) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde.2017.72 · Cited by 19

On Non-Preemptive VM Scheduling in the Cloud

Psychas, K., & Ghaderi, J. (2017) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3154493 · Cited by 19

Edge Federation: A Dependency-Aware Multi-Task Dispatching and Co-location in Federated Edge Container-Instances

Awada, U., & Zhang, J. (2020) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge50951.2020.00021 · Cited by 19

DRMaestro: orchestrating disaggregated resources on virtualized data-centers

Amaral, M., Polo, J., Carrera, D., et al. (2021) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-021-00238-6 · Cited by 19

Flexible Container-Based Computing Platform on Cloud for Scientific Workflows

Liu, K., Aida, K., Yokoyama, S., & Yoshinobu, M. (2016) · International Conference on Cloud Computing Research and Innovation (ICCCRI) · doi:10.1109/icccri.2016.17 · Cited by 19

Kelp: QoS for Accelerated Machine Learning Systems

Zhu, H., Lo, D., Cheng, L., et al. (2019) · Proceedings - International Symposium on High-Performance Computer Architecture/Proceedings · doi:10.1109/hpca.2019.00036 · Cited by 18

Performance analysis of an energy-saving strategy in cloud data centers based on a MMAP[K]/M[K]/ N1+N2 non-preemptive priority queue

Bai, X., & Jin, S. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.06.004 · Cited by 18

Snape: Reliable and Low-Cost Computing with Mixture of Spot and On-Demand VMs

Yang, F., Wang, L., Xu, Z., Zhang, J., Li, L., Qiao, B., Couturier, C., Bansal, C., Ram, S., Qin, S., et al. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3582016.3582028 · Cited by 25

A learning and evolution-based intelligence algorithm for multi-objective heterogeneous cloud scheduling optimization

Hao, Y., Zhao, C., Li, Z., et al. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111366 · Cited by 18

DRPC: Distributed Reinforcement Learning Approach for Scalable Resource Provisioning in Container-Based Clusters

Bai, H., Xu, M., Ye, K., Buyya, R., & Xu, C. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3433388 · Cited by 22

A Big Data analyzer for large trace logs

Balliu, A., Olivetti, D., Babaoğlu, Ö., et al. (2015) · Computing · doi:10.1007/s00607-015-0480-7 · Cited by 18

A Cloud Reservation System for Big Data Applications

Marinescu, D. C., Paya, A., & Morrison, J. P. (2016) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2016.2594783 · Cited by 18

Distributed Monitoring System for Microservices-Based IoT Middleware System

Kang, R., Zhou, Z., Liu, J., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00006-6_43 · Cited by 18

Cloud Workload Characterization and Profiling for Resource Allocation

Dezhabad, N., Ganti, S., & Shoja, G. C. (2019) · IEEE International Conference on Cloud Networking · doi:10.1109/cloudnet47604.2019.9064138 · Cited by 18

KEA: Tuning an Exabyte-Scale Data Infrastructure

Zhu, Y., Krishnan, S., Karanasos, K., et al. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3457569 · Cited by 18

Characterizing Co-located Datacenter Workloads: An Alibaba Case StudyPreprint

Cheng, Y., Chai, Z., & Anwar, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.02919 · Cited by 18

Stateful DRF: Considering the Past in a Multi-Resource Allocation

Sadok, H., Campista, M. E. M., & Costa, L. H. M. K. (2020) · IEEE Transactions on Computers · doi:10.1109/tc.2020.3006007 · Cited by 17

Elasecutor: Elastic Executor Scheduling in Data Analytics Systems

Liu, L., & Xu, H. L. (2021) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2021.3050927 · Cited by 17

Adaptive huge-page subrelease for non-moving memory allocators in warehouse-scale computers

Maas, M., Kennelly, C., Nguyen, K., et al. (2021) · ACM SIGPLAN International Symposium on Memory Management (ISMM) · doi:10.1145/3459898.3463905 · Cited by 17

Towards operator-less data centers through data-driven, predictive, proactive autonomics

Sîrbu, A., & Babaoğlu, Ö. (2016) · Cluster Computing · doi:10.1007/s10586-016-0564-y · Cited by 17

Rethinking High Performance Computing Platforms

Weidner, O., Atkinson, M., Barker, A., et al. (2016) · doi:10.1145/2912152.2912155 · Cited by 17

Exploring the Evolution of Big Data Technologies

Bonner, S., Kureshi, I., Brennan, J., & Theodoropoulos, G. K. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-805467-3.00014-4 · Cited by 17

Towards a Lightweight Multi-Cloud DSL for Elastic and Transferable Cloud-native Applications

Quint, P.-C., & Kratzke, N. (2018) · International Conference on Cloud Computing and Services Science · doi:10.5220/0006683800001896 · Cited by 17

WSMeter

Lee, J., Kim, C., Lin, K., Cheng, L., Govindaraju, R., & Kim, J. (2018) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3173162.3173196 · Cited by 17

Cost-Efficient Orchestration of Containers in Clouds: A Vision, Architectural Elements, and Future Directions

Buyya, R., Rodriguez, M. A., Toosi, A. N., et al. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1108/1/012001 · Cited by 17

Hardware Remediation at Scale

Lin, F., Beadon, M., Dixit, H. D., Vunnam, G., Desai, A., & Sankar, S. (2018) · International Conference on Dependable Systems and Networks workshops · doi:10.1109/dsn-w.2018.00015 · Cited by 17

Online Job Scheduling with Resource Packing on a Cluster of Heterogeneous Servers

Liu, Y., Xu, H., & Lau, W. C. (2019) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2019.8737465 · Cited by 17

Incorporating the Raft consensus protocol in containers managed by Kubernetes: an evaluation

Netto, H. V., Oliveira, C. P., Rech, L. D. O., & Alchieri, E. (2019) · International Journal of Parallel Emergent and Distributed Systems · doi:10.1080/17445760.2019.1608989 · Cited by 17

Neptune

Garefalakis, P., Karanasos, K., & Pietzuch, P. R. (2019) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3357223.3362724 · Cited by 17

Opportunistic scheduling and resources consolidation system based on a new economic model

Menouer, T., Cérin, C., & Hsu, C. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-020-03231-z · Cited by 17

Optimal Resource Allocation of Cloud-Based Spark Applications

Lattuada, M., Barbierato, E., Gianniti, E., et al. (2020) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2020.2985682 · Cited by 17

Challenges for building a cloud native scalable and trustable multi-tenant AIoT platform

Xiong, J., & Chen, H. (2020) · doi:10.1145/3400302.3415756 · Cited by 17

Zero Queueing for Multi-Server Jobs

Wang, W., Xie, Q., & Harchol‐Balter, M. (2021) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3447385 · Cited by 17

Dynamically Negotiating Capacity Between On-demand and Batch Clusters

Liu, F., Keahey, K., Riteau, P., & Weissman, J. (2018) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc.2018.00041 · Cited by 17

Performance overhead of container orchestration frameworks for management of multi-tenant database deployments

Truyen, E., Landuyt, D. V., Lagaisse, B., et al. (2019) · ACM Symposium on Applied Computing · doi:10.1145/3297280.3297536 · Cited by 17

Managing Feature Compatibility in Kubernetes: Vendor Comparison and Analysis

Truyen, E., Kratzke, N., Landuyt, D. V., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3045768 · Cited by 16

FollowMe@LS: Electricity price and source aware resource management in geographically distributed heterogeneous datacenters

Ali, H., Zakarya, M., Rahman, I. U., Khan, A. A., & Buyya, R. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.110907 · Cited by 16

Universal Scaling of Distributed Queues Under Load Balancing in the Super-Halfin-Whitt Regime

Liu, X., & Ying, L. (2021) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2021.3105480 · Cited by 16

Methods for improving the availability of spot instances: A survey

Lin, L., Pan, L., & Liu, S. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103718 · Cited by 16

Not All Resources are Visible

Wang, X., He, H., Li, Y., et al. (2023) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3620678.3624650 · Cited by 16

EasyScale: Elastic Training with Consistent Accuracy and Improved Utilization on GPUs

Li, M., Xiao, W., Yang, H., Sun, B., Zhao, H., Ren, S., Luan, Z., Jia, X., Liu, Y., Li, Y., et al. (2023) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3581784.3607054 · Cited by 31

Robust Large-Scale Machine Learning in the Cloud

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