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Kubernetes as a Standard Container Orchestrator - A Bibliometric Analysis

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Abstract

Container orchestration systems simplify the deployment and maintenance of container-based applications, but developing efficient and well-defined orchestration systems is a challenge. Nowadays, Kubernetes is a leading open-source container orchestration platform that has become the de facto standard. The aim of this paper is to provide a comprehensive overview of the Kubernetes orchestrator and grasp the current research emphasis by using a bibliometric analysis. Bibliometrix software was adopted as bibliometric analysis tools to find hot research topics and guide the future researching in the area. The Web of Science core collection database was used as the primary source for data collection. Data were collected from 803 articles published from 2014 to September 2022. In particular, publication outputs and research areas can provide insight into the development trends and current domains in terms of Kubernetes research. The most influential and productive authors, institutions, countries and journals contributed to this bibliometric analysis. The hottest research topics on Kubernetes are mainly centered on “cloud/fog/edge computing and Internet of Things (IoT)”, “containers and virtualization”, “docker”, “resource scheduling”, “microservices” and “artificial intelligent (AI)”. A cluster analysis was conducted from a keyword perspective to obtain emerging trends and frontiers for Kubernetes. The results showed that future research should focus on “automation”, “5G”, “scalability”, “resource scheduling”, “serverless”, “service mesh” and “blockchain”. Therefore, this paper aims to assist academics and practitioners in gaining a comprehensive understanding of the status quo and trends in Kubernetes research.

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Data Availability

All data generated or analysed during this study are available from the corresponding author on reasonable request.

References

  1. Rodriguez, M.A, Buyya, R: Container-based cluster orchestration systems: a taxonomy and future directions. Software: Prac. Exper. 49(5), 698–719 (2019). https://doi.org/10.1002/spe.2660

    Google Scholar 

  2. Kaiser, S, Haq, M.S, Tosun, A, Korkmaz, T.: Container technologies for ARM architecture: a comprehensive survey of the state-of-the-art, IEEE Access, pp. 1–10, https://doi.org/10.1109/ACCESS.2022.3197151 (2022)

  3. Ambrosino, G., Fioccola, G.B., Canonico, R., Ventre, G.: Container mapping and its impact on performance in containerized Cloud environments. In: 2020 IEEE Int. Conf. on Service Oriented Systems Engineering (SOSE), pp. 57–64, https://doi.org/10.1109/SOSE49046.2020.00014 (2020)

  4. Ogbuachi, M.C., Gore, C., Reale, A., et al: Context-aware K8S scheduler for real time distributed edge computing applications. In: 2019 int. conf. software, telecom. Computer networks (SoftCOM), pp. 1–6, https://doi.org/10.23919/SOFTCOM.2019.8903766 (2019)

  5. The Kubernetes Authors: Kubernetes homepage. Web page, http://kubernetes.io/Accessed2022-09-29(2022)

  6. Hindman, B., Konwinski, A., Zaharia, M., et al: Mesos: a platform for fine-grained resource sharing in the data center. In: Proc. of the 8th USENIX Conf. on Networked Systems Design and Implementation. NSDI’11, pp. 295–308, https://doi.org/10.5555/1972457.1972488 (2011)

  7. Cote, M.: Why Large Organizations Trust Kubernetes. Web page. Accessed 09-29-2022 https://tanzu.vmware.com/content/blog/why-large-organizations-trust-kubernetes (2020)

  8. Arunarani, A., Manjula, D., Sugumaran, V.: Task scheduling techniques in cloud computing: a literature survey. Future Gener. Comput. Syst. 91, 407–415 (2019). https://doi.org/10.1016/j.future.2018.09.014

    Article  Google Scholar 

  9. Kumar, M., Sharma, S. C., Goel, A., Singh, S. P.: A comprehensive survey for scheduling techniques in cloud computing. J. Netw. Comput. Appl. 143, 1–33 (2019). https://doi.org/10.1016/j.jnca.2019.06.006

    Article  Google Scholar 

  10. Kayal, P.: Kubernetes in fog computing: feasibility demonstration, limitations and improvement scope : invited paper. In: 2020 IEEE 6th world forum on internet of things (WF-IoT), pp. 1–6, https://doi.org/10.1109/WF-IoT48130.2020.9221340 (2020)

  11. Matrouk, K., Alatoun, K.: Scheduling algorithms in fog computing: a survey. Int. J. Netw. Distrib. Comput. 9(1), 59–74 (2021). https://doi.org/10.2991/ijndc.k.210111.001

    Article  Google Scholar 

  12. Ahmad, I., AlFailakawi, M. G., AlMutawa, A., Alsalman, L.: Container scheduling techniques: a survey and assessment. J. King Saud University - Comput. Inform. Sci. 34(7), 3934–3947 (2021). https://doi.org/10.1016/j.jksuci.2021.03.002

    Google Scholar 

  13. Carrión, C.: Kubernetes scheduling: taxonomy, ongoing issues and challenges. ACM Comput. Surv., https://doi.org/10.1145/3539606.JustAccepted (2022)

  14. Cobo, M.J., Martinez, M.A., Gutierrez-Salcedo, M., Fujita, H., Herrera-Viedma, E.: 25years at knowledge-based systems: a bibliometric analysis. Knowl.-Based Syst. 80, 3–13 (2015). https://doi.org/10.1016/j.knosys.2014.12.035. 25th anniversary of knowledge-based systems

    Article  Google Scholar 

  15. Aria, M., Cuccurullo, C.: Bibliometrix: an R-tool for comprehensive science mapping analysis. J. Informetrics 11(4), 959–975 (2017). https://doi.org/10.1016/j.joi.2017.08.007

    Article  Google Scholar 

  16. Rashid, A., Chaturvedi, A.: Virtualization and its role in cloud computing environment. Int. J. Comput. Sci. Eng. 7(4), 1131–1136 (2019). https://doi.org/10.26438/ijcse/v7i4.11311136

    Google Scholar 

  17. Pahl, C., Brogi, A., Soldani, J., Jamshidi, P.: Cloud container technologies: a state-of-the-art review. IEEE Trans. Cloud Comput. 7(3), 677–692 (2019). https://doi.org/10.1109/TCC.2017.2702586

    Article  Google Scholar 

  18. The Docker authors: Empowering app development for developer; Docker. Web page. Accessed 28-09-2022, https://www.docker.com/ (2022)

  19. Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., Wilkes, J.: Large-scale cluster management at Google with Borg. In: Proceedings of the tenth European conf. on computer systems. EuroSys ’15. 10.1145/2741948.2741964 (2015)

  20. CNCF: Cloud native computing foundation charter Web page, https://www.cncf.io/about/charter/(2022)

  21. Mora Soler, S.: Analysis and implementation of a cloud environment for microservices based on Kubernetes and Istio Master’s degreee in engineering computer science, higher school of computer engineering university of Castilla-La Mancha (2019)

  22. Chhajed, S.: Learning ELK stack packt publishing ltd (2015)

  23. Chen, Y.-S., Leimkuhler, F.F.: A relationship between lotka’s law, bradford’s law, and zipf’s law. J. American Soc. Inform. Sci. 37(5), 307–314 (1986). https://doi.org/10.1002/(SICI)1097-4571(198609)37:5<307::AID-ASI5>3.0.CO;2-8

    Article  Google Scholar 

  24. Van Eck, N., Waltman, L.: Software survey: VOSviewer, a computer program for bibliometric mapping. scientometrics 84(2), 523–538 (2010). https://doi.org/10.1007/s11192-009-0146-3

    Article  Google Scholar 

  25. Chen, C.: CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J. American Soc. Informat Sci. Technol. 57(3), 359–377 (2006). https://doi.org/10.1002/asi.20317

    Article  Google Scholar 

  26. Li, J., Goerlandt, F., Reniers, G.: n overview of scientometric mapping for the safety science community: methods, tools, and framework. Safety Sci. 134, 105093 (2021). https://doi.org/10.1016/j.ssci.2020.105093

    Article  Google Scholar 

  27. Alyas, T., Tabassum, N., Iqbal, M.W., Alshahrani, A.S., Alghamdi, A., Shahzad, S.K.: Resource based automatic calibration system (rbacs) using kubernetes framework. Intell. Autom. Sft. Comput. 35(1), 1165–1179 (2023). https://doi.org/10.32604/iasc.2023.028815

    Article  Google Scholar 

  28. Castro Leon, J., Team, C.C.I: Advanced features of the cern openstack cloud. In: 23rd international conference on computing in high energy and nuclear physics (CHEP 2018). EPJ web of conferences, vol. 214, https://doi.org/10.1051/epjconf/201921407026 (2019)

  29. Augustyn, D.R., Wycislik, L., Sojka, M.: The cloud-enabled architecture of the clinical data repository in poland. Sustainability, vol. 13(24), https://doi.org/10.3390/su132414050 (2021)

  30. Bornstein, Y., Dayan, B., Cahn, A., Wells, S., Housh, M.: Environmental decision support systems as a service: demonstration on ce-qual-w2 model. Water, vol. 14(6), https://doi.org/10.3390/w14060885 (2022)

  31. Bernstein, D.: Containers and cloud:f From lxc to docker to kubernetes. IEEE Cloud Comput. 1(3), 81–84 (2014). https://doi.org/10.1109/MCC.2014.51

    Article  Google Scholar 

  32. Arnautov, S., Trach, B., Gregor, F., Knauth, T., Martin, A., Priebe, C., Lind, J., Muthukumaran, D., O’keeffe, D., Stillwell, M. L., et al: SCONE: secure linux containers with intel SGX. In: 12Th USENIX symposium on operating systems design and implementation (OSDI 16), pp. 689–703 (2016)

  33. Burns, B., Grant, B., Oppenheimer, D., Brewer, E., Wilkes, J.: Borg, omega, and kubernetes. Commun. ACM 59, 50–57 (2016). https://doi.org/10.1145/2890784

    Article  Google Scholar 

  34. Stanciu, A.: Blockchain based distributed control system for edge computing. In: 2017 21st int. conf. on control systems and computer science (CSCS), pp. 667–671, https://doi.org/10.1109/CSCS.2017.102 (2017)

  35. Peng, Y., Bao, Y., Chen, Y., Wu, C., Guo, C.: Optimus: an efficient dynamic resource scheduler for deep learning clusters. In: EUROSYS ‘18: Proc. of the 13th Eurosys conference, https://doi.org/10.1145/3190508.3190517 (2018)

  36. Guerrero, C., Lera, I., Juiz, C.: Genetic algorithm for multi-objective optimization of container allocation in cloud architecture. J. Grid Comput. 16(1, SI), 113–135 (2018). https://doi.org/10.1007/s10723-017-9419-x

    Article  Google Scholar 

  37. Joy, A.M.: Performance comparison between linux containers and virtual machines. In: 2015 international conference on advances in computer engineering and applications, pp. 342–346, https://doi.org/10.1109/ICACEA.2015.7164727 (2015)

  38. Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., Merle, P.: Autonomic vertical elasticity of docker containers with ElasticDocker. In: 2017 IEEE 10th international conference on cloud computing (CLOUD), pp. 472–479. IEEE, https://doi.org/10.1109/CLOUD.2017.67 (2017)

  39. Kratzke, N., Quint, P.-C.: Understanding cloud-native applications after 10 years of cloud computing - a systematic mapping study. J. Syst. Software 126, 1–16 (2017). https://doi.org/10.1016/j.jss.2017.01.001

    Article  Google Scholar 

  40. Blaiszik, B., Ward, L., Schwarting, M., Gaff, J., Chard, R., Pike, D., Chard, K., Foster, I.: A data ecosystem to support machine learning in materials science. MRS Commun. 9(4), 1125–1133 (2019). https://doi.org/10.1557/mrc.2019.118

    Article  Google Scholar 

  41. Manvi, S., Krishna Shyam, G.: Resource management for infrastructure as a service (iaas) in cloud computing: a survey. J. Netw. Comput. Appl. 41, 424–440 (2014). https://doi.org/10.1016/j.jnca.2013.10.004

    Article  Google Scholar 

  42. Musaddiq, A., Zikria, Y., Hahm, O., Yu, H., Bashir, A., Kim, S.: A survey on resource management in IoT operating systems. IEEE Access 6, 8459–8482 (2018). https://doi.org/10.1109/ACCESS.2018.2808324

    Article  Google Scholar 

  43. Ghobaei-Arani, M., Souri, A., Rahmanian, A.: Resource management approaches in fog computing: a comprehensive review. J. Grid Comput. 18(1), 1–42 (2020). https://doi.org/10.1007/s10723-019-09491-1

    Article  Google Scholar 

  44. Balalaie, A., Heydarnoori, A., Jamshidi, P.: Microservices architecture enables devops: migration to a cloud-native architecture. IEEE Soft. 33(3), 42–52 (2016). https://doi.org/10.1109/MS.2016.64

    Article  Google Scholar 

  45. Naha, R.K., Garg, S., Georgakopoulos, D., Jayaraman, P.P., Gao, L., Xiang, Y., Ranjan, R.: Fog computing: survey of trends, architectures, requirements, and research directions. IEEE Access 6, 47980–48009 (2018). https://doi.org/10.1109/ACCESS.2018.2866491

    Article  Google Scholar 

  46. Al-Doghman, F., Moustafa, N., Khalil, I., Tari, Z., Zomaya, A.: Ai-enabled secure microservices in edge computing: opportunities and challenges. IEEE Trans. Serv. Comput.:1–1, https://doi.org/10.1109/TSC.2022.3155447 (2022)

  47. Mahmud, M., Kaiser, M.S., Hussain, A., Vassanelli, S.: Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2063–2079 (2018). https://doi.org/10.1109/TNNLS.2018.2790388

    Article  MathSciNet  Google Scholar 

  48. Harichane, I., Makhlouf, A., Belalem, G.: A proposal of kubernetes scheduler using machine-learning on cpu/gpu cluster. In: Silhavy, R. (ed.) Intelligent algorithms in soft. Engineering, pp. 567–580. Springer, https://doi.org/10.1007/978-3-030-51965-0∖_50 (2020)

  49. Peng, Y., Bao, Y., Chen, Y., Wu, C., Meng, C., Lin, W.: Dl2: a deep learning-driven scheduler for deep learning clusters. IEEE Trans. Parall. Distrib. Syst. 32(8), 1947–1960 (2021). https://doi.org/10.1109/TPDS.2021.3052895

    Article  Google Scholar 

  50. Netto, H.V., Lung, L.C., Correia, M., Luiz, A.F., Sá de Souza, L.M.: State machine replication in containers managed by kubernetes. J. Syst. Architect. 73, 53–59 (2017). https://doi.org/10.1016/j.sysarc.2016.12.007. Special issue on reliable software technologies for dependable distributed systems

    Article  Google Scholar 

  51. Santos, J., Wauters, T., Volckaert, B., De Turck, F.: Towards network-aware resource provisioning in kubernetes for fog computing applications. In: 2019 IEEE conf. on network softwarization (NetSoft), pp. 351–359, https://doi.org/10.1109/NETSOFT.2019.8806671 (2019)

  52. Chang, C., Yang, S., Yeh, E., Lin, P., Jeng, J.: A kubernetes-based monitoring platform for dynamic cloud resource provisioning. In: GLOBECOM 2017 - 2017 IEEE global communications conf., pp. 1–6, https://doi.org/10.1109/GLOCOM.2017.8254046 (2017)

  53. Nguyen, T., Yeom, Y., Kim, T., Park, D., Kim, S.: Horizontal pod autoscaling in kubernetes for elastic container orchestration. Sensors, vol. 20(16). https://doi.org/10.3390/s20164621 (2020)

  54. Zhao, H., Deng, S., Liu, Z., Yin, J., Dustdar, S.: Distributed redundant placement for microservice-based applications at the edge. IEEE Trans. Serv. Comput. 15(3), 1732–1745 (2022). https://doi.org/10.1109/TSC.2020.3013600

    Article  Google Scholar 

  55. Chaudhry, S.R., Palade, A., Kazmi, A., Clarke, S.: Improved qos at the edge using serverless computing to deploy virtual network functions. IEEE Inter. Things J. 7(10), 10673–10683 (2020). https://doi.org/10.1109/JIOT.2020.3011057

    Article  Google Scholar 

  56. Wiranata, F.A., Shalannanda, W., Mulyawan, R., Adiono, T.: Automation of virtualized infrastructure using mosaic operator over kubernetes supporting network slicing. In: 2020 14th international conference on telecommunication systems, services, and applications (TSSA, pp. 1–5, https://doi.org/10.1109/TSSA51342.2020.9310895 (2020)

  57. Espinel Sarmiento, D., Lebre, A., Nussbaum, L., Chari, A.: Decentralized sdn control plane for a distributed cloud-edge infrastructure: a survey. IEEE Commun. Surv. Tutor. 23(1), 256–281 (2021). https://doi.org/10.1109/COMST.2021.3050297

    Article  Google Scholar 

  58. Okwuibe, J., Haavisto, J., Harjula, E., Ahmad, I., Ylianttila, M.: Sdn enhanced resource orchestration of containerized edge applications for industrial iot. IEEE Access 8, 229117–229131 (2020). https://doi.org/10.1109/ACCESS.2020.3045563

    Article  Google Scholar 

  59. Li, Z., Guo, L., Cheng, J., Chen, Q., He, B., Guo, M.: The serverless computing survey: a technical primer for design architecture. ACM Comput. Surv., vol. 54(10s) (2022)

  60. Govind, H., Gonzalez–Velez, H.: Benchmarking serverless workloads on kubernetes. In: 2021 IEEE/ACM 21st int. symposium on cluster, cloud and internet computing (CCGrid), pp. 704–712, https://doi.org/10.1109/CCGrid51090.2021.00085 (2021)

  61. Djemame, K., Parker, M., Datsev, D.: Open-source serverless architectures: an evaluation of apache openwhisk. In: 2020 IEEE/ACM 13th int. conf. on utility and cloud computing (UCC), pp. 329–335, https://doi.org/10.1109/UCC48980.2020.00052 (2020)

  62. Mohanty, S., Premsankar, G., di Francesco, M.: An evaluation of open source serverless computing frameworks. In: 2018 IEEE int. conf. on cloud computing technology and science (CloudCom), pp. 115–120, https://doi.org/10.1109/CloudCom2018.2018.00033 (2018)

  63. Shahrad, M., Balkind, J., Wentzlaff, D.: Architectural implications of function-as-a-service computing. In: Proc. 52nd annual IEEE/ACM int. symposium on microarchitecture. MICRO ’52, pp. 1063–1075, https://doi.org/10.1145/3352460.3358296 (2019)

  64. Tzenetopoulos, A., Apostolakis, E., Tzomaka, A., Papakostopoulos, C., Stavrakakis, K., Katsaragakis, M., Oroutzoglou, I., Masouros, D., Xydis, S., Soudris, D.: Faas and curious: performance implications of serverless functions on edge computing platforms. In: High performance computing: ISC high performance digital 2021 international workshops, pp. 428–438, https://doi.org/10.1007/978-3-030-90539-2∖_29 (2021)

  65. Kjorveziroski, V., Filiposka, S.: Kubernetes distributions for the edge: serverless performance evaluation. J. Supercomput. 78(11), 13728–13755 (2022). https://doi.org/10.1007/s11227-022-04430-6

    Article  Google Scholar 

  66. Pääkkönen, P., Pakkala, D., Kiljander, J., Sarala, R.: Architecture for enabling edge inference via model transfer from cloud domain in a kubernetes environment. Future Internet, vol. 13(1), https://doi.org/10.3390/fi13010005 (2021)

  67. Baresi, L., Quattrocchi, G.: Paps: a serverless platform for edge computing infrastructures. Front. Sustain. Cities 3, 690660 (2021). https://doi.org/10.3389/frsc.2021.690660

    Article  Google Scholar 

  68. Risco, S., Moltó, G., Naranjo, D.M., Blanquer, I.: Serverless workflows for containerised applications in the cloud continuum. J. Grid Comput., vol. 19(3), https://doi.org/10.1007/s10723-021-09570-2 (2021)

  69. Hussain, F., Li, W., Noye, B., Sharieh, S., Ferworn, A.: Intelligent service mesh framework for api security and management. In: 2019 IEEE 10th annual information technology, electronics and mobile communication conference (IEMCON), pp. 0735–0742, https://doi.org/10.1109/IEMCON.2019.8936216 (2019)

  70. Dab, B., Fajjari, I., Rohon, M., Auboin, C., Diquélou, A.: An efficient traffic steering for cloud-native service function chaining. In: 2020 23rd conference on innovation in clouds, internet and networks and workshops (ICIN), pp. 71–78, https://doi.org/10.1109/ICIN48450.2020.9059340 (2020)

  71. Ganguli, M., Ranganath, S., Ravisundar, S., Layek, A., Ilangovan, D., Verplanke, E.: Challenges and opportunities in performance benchmarking of service mesh for the edge. In: 2021 IEEE international conference on edge computing (EDGE), pp. 78–85, https://doi.org/10.1109/EDGE53862.2021.00020 (2021)

  72. Rodigari, S., O;Shea, D., McCarthy, P., McCarry, M., McSweeney, S.: Performance analysis of zero-trust multi-cloud. In: 2021 IEEE 14th international conference on cloud computing (CLOUD), pp. 730–732, https://doi.org/10.1109/CLOUD53861.2021.00097 (2021)

  73. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Technical report, https://bitcoin.org/bitcoin.pdf (2008)

  74. Buterin, V.: A next-generation smart contract and decentralized application platform. Technical report. https://cryptorating.eu/whitepapers/Ethereum/Ethereum_white_paper.pdfhttps://cryptorating.eu/whitepapers/Ethereum/Ethereum_white_paper.pdf (2013)

  75. Szabo, N.: Smart contracts: building blocks for digital markets. EXTROPY: J Transhumanist Thought, vol. 16(18(2)) (1996)

  76. Tosh, D., Shetty, S., Foytik, P., Kamhoua, C., Njilla, L.: Cloudpos: a Proof-Of-Stake Consensus Design for Blockchain Integrated Cloud. In: 2018 IEEE 11Th international conference on cloud computing (CLOUD), pp. 302–309, https://doi.org/10.1109/CLOUD.2018.00045 (2018)

  77. Sun, J., Wu, C., Ye, J.: Blockchain-based automated container cloud security enhancement system. In: 2020 IEEE international conference on smart cloud (SmartCloud), pp. 1–6, https://doi.org/10.1109/SmartCloud49737.2020.00010 (2020)

  78. Górski, T.: Towards continuous deployment for blockchain. Appl. Sci., vol. 11(24). https://doi.org/10.3390/app112411745 (2021)

  79. Kanagachalam, S., Tulkinbekov, K., Kim, D.-H.: Blosm: blockchain-based service migration for connected cars in embedded edge environment. Electron., vol. 11(3), https://doi.org/10.3390/electronics11030341 (2022)

  80. Nasir, A., Shaukat, K., Khan, K.I., Hameed, I.A., Alam, T.M., Luo, S.: What is core and what future holds for blockchain technologies and cryptocurrencies: a bibliometric analysis. EEE Access 9, 989–1004 (2021). https://doi.org/10.1109/ACCESS.2020.3046931

    Google Scholar 

  81. Campra, M., Riva, P., Oricchio, G., Brescia, V.: Bibliometrix analysis of medical tourism. Health Serv. Manag. Res. 35(3), 172–188 (2022). https://doi.org/10.1177/0951484821101.1738

    Article  Google Scholar 

  82. Rejeb, A., Rejeb, K., Abdollahi, A., Al-Turjman, F., Treiblmaier, H.: The interplay between the internet of things and agriculture: a bibliometric analysis and research agenda. Internet Things 19, 100580 (2022). https://doi.org/10.1016/j.iot.2022.100580

    Article  Google Scholar 

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Funding

This work has been funded by MCIN/AEI/10.13039/501100011033 and by European Regional Development Fund (ERDF), “A way to make Europe” (ref. PID2021-123627OB-C52), and under GC-020-017 grant, funded by the Regional Government of Castilla-La Mancha for Consolidated Research Groups.

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    Carmen Carrión

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Carrión, C. Kubernetes as a Standard Container Orchestrator - A Bibliometric Analysis. J Grid Computing 20, 42 (2022). https://doi.org/10.1007/s10723-022-09629-8

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