Skip to main content

Advertisement

Springer Nature Link
Log in

Kubernetes in IT administration and serverless computing: An empirical study and research challenges

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Today’s industry has gradually realized the importance of lifting efficiency and saving costs during the life-cycle of an application. In particular, we see that most of the cloud-based applications and services often consist of hundreds of micro-services; however, the traditional monolithic pattern is no longer suitable for today’s development life-cycle. This is due to the difficulties of maintenance, scale, load balance, and many other factors associated with it. Consequently, people switch their focus on containerization—a lightweight virtualization technology. The saving grace is that it can use machine resources more efficiently than the virtual machine (VM). In VM, a guest OS is required to simulate on the host machine, whereas containerization enables applications to share a common OS. Furthermore, containerization facilitates users to create, delete, or deploy containers effortlessly. In order to manipulate and manage the multiple containers, the leading Cloud providers introduced the container orchestration platforms, such as Kubernetes, Docker Swarm, Nomad, and many others. In this paper, a rigorous study on Kubernetes from an administrator’s perspective is conducted. In a later stage, serverless computing paradigm was redefined and integrated with Kubernetes to accelerate the development of software applications. Theoretical knowledge and experimental evaluation show that this novel approach can be accommodated by the developers to design software architecture and development more efficiently and effectively by minimizing the cost charged by public cloud providers (such as AWS, GCP, Azure). However, serverless functions are attached with several issues, such as security threats, cold start problem, inadequacy of function debugging, and many other. Consequently, the challenge is to find ways to address these issues. However, there are difficulties and hardships in addressing all the issues altogether. Respectively, in this paper, we simply narrow down our analysis toward the security aspects of serverless. In particular, we quantitatively measure the success probability of attack in serverless (using Attack Tree and Attack–Defense Tree) with the possible attack scenarios and the related countermeasures. Thereafter, we show how the quantification can reflect toward the end-to-end security enhancement. In fine, this study concludes with research challenges such as the burdensome and error-prone steps of setting the platform, and investigating the existing security vulnerabilities of serverless computing, and possible future directions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. ADTool https://satoss.uni.lu/members/piotr/adtool/

  2. Namespace encapsulates the Kernel’s global resources, so that each namespace can have an independent and isolated resource sets. It helps ensure different processes to use the same resource in their namespaces without interfering with each other.

  3. Docker Hub is a registry service on the cloud that allows developers to download Docker images that are built by other communities.

  4. Pods is a collection of containers whose functions are highly related.

  5. Volume is a store technique, which maps local file to container’s directory. It enables developers to modify local code and dynamic update the container.

  6. Ubuntu https://ubuntu.com/

  7. Kubernetes The Hard Way https://github.com/kelseyhightower/kubernetes-the-hard-way

  8. Vagrant is automatic tool for creating and managing multiple VMs at the same time.

  9. CoreDNS is a DNS server. It is written in Go. It can be used in a multitude of environments because of its flexibility.

  10. Kubeadm Way https://kubernetes.io/docs/setup/production-environment/tools/kubeadm/install-kubeadm/

  11. High availability: HA means that developers usually create more than one node for some services to prevent the emergency. If one node breaks down, then other nodes can back up and work.

  12. Grafana Dashboard https://grafana.com/grafana/dashboards

  13. hey https://github.com/rakyll/hey

  14. ADTool https://satoss.uni.lu/members/piotr/adtool/

Abbreviations

AWS :

Amazon Web Service

ASAP :

Autonomous System Adaptation Platform

CAS :

Content-Addressable Storage

CI :

Continuous Integration

CD :

Continuous Deployment

CLI :

Command Line Interface

CNI :

Container Network Interface

CA :

Certificate Authority

DRIP :

Dynamic Real-time Infrastructure Planner

FaaS :

Function as a Service

GCP :

Google Cloud Platform

HA :

High Availability

IoT :

Internet Of Thing

IFC :

Information Flow Control

IaaS :

Infrastructure as a Service

K8S :

Kubernetes

HPA :

Horizontal Pod Autoscaler

MPSC :

Multi-Provider Serverless Computing

P2P :

Peer-to-Peer

SaaS :

Software as a Service

SWITCH :

Software Workbench for Interactive, Time Critical and Highly self-adaptive Cloud applications

SLO :

Service-Level Objective

SIDE :

SWITCH Interactive Development Environment

VM :

Virtual Machine

ATree :

Attack Tree

ADTree :

Attack–Defense Tree

References

  1. Naik N (2016) Building a virtual system of systems using docker swarm in multiple clouds. In: 2016 IEEE International Symposium on Systems Engineering (ISSE), IEEE, pp 1–3

  2. Guerrero C, Lera I, Juiz C (2018) Resource optimization of container orchestration: a case study in multi-cloud microservices-based applications. The Journal of Supercomputing 74(7):2956–2983

    Article  Google Scholar 

  3. Cérin C, Menouer T, Saad W, Abdallah WB (2017) A new docker swarm scheduling strategy. In: 2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2), IEEE, pp 112–117

  4. Soppelsa F, Kaewkasi C (2016) Native docker clustering with swarm. Packt Publishing Ltd

  5. Burns B, Grant B, Oppenheimer D, Brewer E, Wilkes J (2016) Borg, omega, and kubernetes: lessons learned from three container-management systems over a decade. Queue 14(1):70–93

    Article  Google Scholar 

  6. Bernstein D (2014) Containers and cloud: from lxc to docker to kubernetes. IEEE Cloud Computing 1(3):81–84

    Article  Google Scholar 

  7. Ifrah S (2019) Deploy a containerized application with amazon EKS. In: Deploy Containers on AWS, Springer, pp 135–173

  8. Bisong E (2019) Containers and google kubernetes engine. In: Building Machine Learning and Deep Learning Models on Google Cloud Platform, Springer, pp 655–670

  9. Ifrah S (2021) Deploy containerized applications with google kubernetes engine (GKE). In: Getting Started with Containers in Google Cloud Platform, Springer, pp 105–135

  10. Orchestration C, Buchanan S, Rangama J, Bellavance N (2019) Introducing Azure Kubernetes Service. Springer, Berline

    Google Scholar 

  11. Baldini I, Castro P, Chang K, Cheng P, Fink S, Ishakian V, Mitchell N, Muthusamy V, Rabbah R, Slominski A, et al. (2017) Serverless computing: current trends and open problems. In: Research Advances in Cloud Computing, Springer, pp 1–20

  12. Mohanty SK, Premsankar G, Di Francesco M, et al. (2018) An evaluation of open source serverless computing frameworks. In: CloudCom, pp 115–120

  13. Back T, Andrikopoulos V (2018) Using a microbenchmark to compare function as a service solutions. In: European Conference on Service-Oriented and Cloud Computing, Springer, pp 146–160

  14. Fox GC, Ishakian V, Muthusamy V, Slominski A (2017) Status of serverless computing and function-as-a-service (faas) in industry and research. arXiv preprint arXiv:170808028

  15. Shahrad M, Balkind J, Wentzlaff D (2019) Architectural implications of function-as-a-service computing. In: Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, pp 1063–1075

  16. Schneier B (1999) Attack trees. Dr Dobb’s journal 24(12):21–29

    Google Scholar 

  17. Schneier B (2015) Secrets and lies: digital security in a networked world. Wiley, NewYork

    Book  Google Scholar 

  18. Kordy B, Mauw S, Radomirović S, Schweitzer P (2014) Attack-defense trees. J Logic Comput 24(1):55–87

    Article  MathSciNet  Google Scholar 

  19. Audinot M, Pinchinat S, Kordy B (2017) Is my attack tree correct? In: European Symposium on Research in Computer Security, Springer, pp 83–102

  20. Roy A, Kim DS, Trivedi KS (2012) Attack countermeasure trees (ACT): towards unifying the constructs of attack and defense trees. Security Commun Netw 5(8):929–943

    Article  Google Scholar 

  21. Kordy P, Schweitzer P (2012) The ADTool Manual. University of Luxembourg

  22. Kordy B, Kordy P, Mauw S, Schweitzer P (2013) ADTool: security analysis with attack–defense trees. In: International conference on quantitative evaluation of systems, Springer, pp 173–176

  23. Gadyatskaya O, Jhawar R, Kordy P, Lounis K, Mauw S, Trujillo-Rasua R (2016) Attack trees for practical security assessment: ranking of attack scenarios with ADTool 2.0. In: International Conference on Quantitative Evaluation of Systems, Springer, pp 159–162

  24. Kubernetes\(\_\)Official\(\_\)Documentation (2021) What is Kubernetes? https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/

  25. Grider S (2019) Docker and kubernetes: the complete guide https://www.udemy.com/course/docker-and-kubernetes-the-complete-guide/learn/lecture/11437326#overview

  26. Hightower K, Burns B, Beda J (2017) Kubernetes: up and running: dive into the future of infrastructure. “ O’Reilly Media, Inc.”

  27. Truyen E, Van Landuyt D, Preuveneers D, Lagaisse B, Joosen W (2019) A comprehensive feature comparison study of open-source container orchestration frameworks. Appl Sci 9(5):931

    Article  Google Scholar 

  28. Al Jawarneh IM, Bellavista P, Bosi F, Foschini L, Martuscelli G, Montanari R, Palopoli A (2019) Container orchestration engines: a thorough functional and performance comparison. In: ICC 2019-2019 IEEE International Conference on Communications (ICC), IEEE, pp 1–6

  29. Acuña P (2016) Amazon EC2 container service. Deploying rails with docker. Springer, Kubernetes and ECS, pp 69–98

    Chapter  Google Scholar 

  30. Ifrah S (2019) Deploying containerized applications with amazon ECS. In: Deploy Containers on AWS, Springer, pp 83–133

  31. Pousty S, Miller K (2014) Getting Started with OpenShift: a Guide for Impatient Beginners. “ O’Reilly Media, Inc.”

  32. Lossent A, Peon AR, Wagner A (2017) PaaS for web applications with OpenShift Origin. In: J Phys: Conf Series, IOP Publishing, vol 898, p 082037

  33. Aly M, Khomh F, Yacout S (2018) Kubernetes or openShift? Which technology best suits eclipse hono IoT deployments. In: 2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA), IEEE, pp 113–120

  34. MANGAT M (2021) Best container orchestration tools for 2020 https://phoenixnap.com/blog/container-orchestration-tools

  35. Pan Y, Chen I, Brasileiro F, Jayaputera G, Sinnott R (2019) A performance comparison of cloud-based container orchestration tools. In: 2019 IEEE International Conference on Big Knowledge (ICBK), IEEE, pp 191–198

  36. Naser H (2017) Kubernetes Vs. mesos: a comparison of containerization platforms part II https://vexxhost.com/blog/kubernetes-mesos-comparison-containerization/

  37. Modak A, Chaudhary S, Paygude P, Ldate S (2018) Techniques to secure data on cloud: docker swarm or kubernetes? In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), IEEE, pp 7–12

  38. Nomad (2020) Nomad vs. Kubernetes https://www.nomadproject.io/intro/vs/kubernetes/

  39. Linzel B, Zhu E, Flores G, Liu J, Dikaleh S (2019) How can OpenShift accelerate your Kubernetes adoption: a workshop exploring openShift features. In: Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering, pp 380–381

  40. Anderson C (2015) Docker [software engineering]. IEEE Software 32(3):102-c3

    Article  Google Scholar 

  41. Martin JP, Kandasamy A, Chandrasekaran K (2018) Exploring the support for high performance applications in the container runtime environment. Human-centric Comput Inf Sci 8(1):1–15

    Article  Google Scholar 

  42. Xie XL, Wang P, Wang Q (2017) The performance analysis of Docker and rkt based on Kubernetes. 2017 13th International Conference on Natural Computation. Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), IEEE, pp 2137–2141

  43. Arundel J, Domingus J (2019) Cloud native devops with kubernetes: building, deploying, and scaling modern applications in the cloud. O’Reilly Media

  44. Saito H, Lee HCC, Wu CY (2019) DevOps with Kubernetes: accelerating software delivery with container orchestrators. Packt Publishing Ltd

  45. Kubernetes\(\_\)Official\(\_\)Documentation (2021) Kubernetes Components https://kubernetes.io/docs/concepts/overview/components/

  46. Mannambeth M (2020) Certified kubernetes administrator (CKA) with practice tests. https://www.udemy.com/course/certified-kubernetes-administrator-with-practice-tests/learn/lecture/14296142

  47. Kumar R, Trivedi MC (2021) Networking analysis and performance comparison of kubernetes CNI Plugins. Advances in computer. Springer, Berline, pp 99–109

    Google Scholar 

  48. Boettiger C (2015) An introduction to Docker for reproducible research. ACM SIGOPS Oper Syst Rev 49(1):71–79

    Article  Google Scholar 

  49. Belmont JM (2018) Hands-On continuous integration and delivery: build and release quality software at scale with Jenkins, Travis CI, and CircleCI. Packt Publishing Ltd

  50. Sewak M, Singh S (2018) Winning in the era of serverless computing and function as a service. In: 2018 3rd International Conference for Convergence in Technology (I2CT), pp 1–5

  51. Eivy A (2017) Be wary of the economics of“ Serverless’’ cloud computing. IEEE Cloud Comput 4(2):6–12

    Article  Google Scholar 

  52. Van Eyk E, Toader L, Talluri S, Versluis L, Uţă A, Iosup A (2018) Serverless is more: from PaaS to present cloud computing. IEEE Internet Comput 22(5):8–17

    Article  Google Scholar 

  53. Yan M, Castro P, Cheng P, Ishakian V (2016) Building a chatbot with serverless computing. In: Proceedings of the 1st International Workshop on Mashups of Things and APIs, pp 1–4

  54. Spillner J (2017) Snafu: Function-as-a-service (faas) runtime design and implementation. arXiv preprint arXiv:170307562

  55. Kuntsevich A, Nasirifard P, Jacobsen HA (2018) A distributed analysis and benchmarking framework for apache openwhisk serverless platform. In: Proceedings of the 19th International Middleware Conference (Posters), pp 3–4

  56. Djemame K, Parker M, Datsev D (2020) Open-source serverless architectures: an Evaluation of Apache OpenWhisk. In: 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC), IEEE, pp 329–335

  57. Ellis A (2019) The power of interfaces in OpenFaaS https://blog.alexellis.io/the-power-of-interfaces-openfaas/

  58. Kaewkasi C (2018) Docker for serverless applications: containerize and orchestrate functions using OpenFaas, OpenWhisk, and Fn. Packt Publishing Ltd

  59. Prometheus\(\_\)Official\(\_\)Documentation (2021) What is Prometheus? https://prometheus.io/docs/introduction/overview/

  60. Sabharwal N, Pandey P (2020) Getting started with prometheus and alert manager. In: Monitoring Microservices and Containerized Applications, Springer, pp 43–83

  61. Turnbull J (2018) Monitoring with Prometheus. Turnbull Press

  62. Brazil B (2018) Prometheus: up & running: infrastructure and application performance monitoring. “ O’Reilly Media, Inc.”

  63. Brattstrom M, Morreale P (2017) Scalable agentless cloud network monitoring. In: 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud), IEEE, pp 171–176

  64. Nguyen N, Kim T (2020) Toward highly scalable load balancing in kubernetes clusters. IEEE Commun Mag 58(7):78–83

    Article  Google Scholar 

  65. Trivedi KS, Sahner R (2009) SHARPE at the age of twenty two. ACM SIGMETRICS Perform Eval Rev 36(4):52–57

    Article  Google Scholar 

  66. Ou X, Singhal A (2011) Quantitative security risk assessment of enterprise networks. Springer, Berlin

    Book  Google Scholar 

  67. Henley EJ, Kumamoto H (1996) Probabilistic risk assessment and management for engineers and scientists. IEEE Press (2nd Edition)

  68. Stallings W, Brown L, Bauer MD, Bhattacharjee AK (2012) Computer security: principles and practice. Pearson Education Upper Saddle River, NJ, USA

    Google Scholar 

  69. Hubbard DW, Seiersen R (2016) How to measure anything in cybersecurity risk. Wiley Online Library

  70. Ingoldsby TR (2010) Attack tree-based threat risk analysis. Amenaza Technologies Limited pp 3–9

  71. Coles-Kemp L, Bullée JW, Montoya L, Junger M, Heath C, Pieters W, Wolos L (2015) Technology-supported Risk Estimation by Predictive Assessment of Socio-technical Security

  72. Alpernas K, Flanagan C, Fouladi S, Ryzhyk L, Sagiv M, Schmitz T, Winstein K (2018) Secure serverless computing using dynamic information flow control. arXiv preprint arXiv:180208984

  73. Bacon J, Eyers D, Pasquier TFM, Singh J, Papagiannis I, Pietzuch P (2014) Information flow control for secure cloud computing. IEEE Transac Netw Service Manag 11(1):76–89

    Article  Google Scholar 

  74. Alpernas K, Flanagan C, Fouladi S, Ryzhyk L, Sagiv M, Schmitz T, Winstein K (2018) Secure serverless computing using dynamic information flow control. Proc ACM Program Lang 2(OOPSLA), https://doi.org/10.1145/3276488

  75. O’Meara W, Lennon RG (2020) Serverless computing security: protecting application logic. In: 2020 31st Irish Signals and Systems Conference (ISSC), IEEE, pp 1–5

  76. Podjarny G (2019) Serverless Security. O’Reilly Media Inc

  77. Li X, Leng X, Chen Y (2021) Securing serverless computing: challenges, solutions, and opportunities. arXiv preprint arXiv:210512581

  78. Datta P, Kumar P, Morris T, Grace M, Rahmati A, Bates A (2020) Valve: securing function workflows on serverless computing platforms. Proc The Web Conf 2020:939–950

    Google Scholar 

  79. Kelly D, Glavin FG, Barrett E (2021) Denial of wallet-defining a looming threat to serverless computing. J Inform Security Appl 60:102843

    Google Scholar 

  80. Jonas E, Schleier-Smith J, Sreekanti V, Tsai CC, Khandelwal A, Pu Q, Shankar V, Carreira J, Krauth K, Yadwadkar N, et al. (2019) Cloud programming simplified: a berkeley view on serverless computing. arXiv preprint arXiv:190203383

  81. Ruddle A, Ward D, Weyl B, Idrees S, Roudier Y, Friedewald M, Leimbach T, Fuchs A, Gürgens S, Henniger O, et al. (2009) Deliverable D2. 3: Security requirements for automotive on-board networks based on dark-side scenarios. EVITA project

  82. Guo J, Rahimi M, Cleland-Huang J, Rasin A, Hayes JH, Vierhauser M (2016) Cold-start software analytics. In: Proceedings of the 13th International Conference on Mining Software Repositories, pp 142–153

  83. Štefanič P, Cigale M, Jones AC, Knight L, Taylor I, Istrate C, Suciu G, Ulisses A, Stankovski V, Taherizadeh S et al (2019) SWITCH workbench: a novel approach for the development and deployment of time-critical microservice-based cloud-native applications. Future Gener Comput Syst 99:197–212

    Article  Google Scholar 

  84. Ghosh BC, Addya SK, Somy NB, Nath SB, Chakraborty S, Ghosh SK (2020) Caching techniques to improve latency in serverless architectures. In: 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS), IEEE, pp 666–669

  85. Hall A, Ramachandran U (2019) An execution model for serverless functions at the edge. In: Proceedings of the International Conference on Internet of Things Design and Implementation, pp 225–236

  86. Nguyen HD, Zhang C, Xiao Z, Chien AA (2019) Real-time serverless: enabling application performance guarantees. In: Proceedings of the 5th International Workshop on Serverless Computing, pp 1–6

  87. Du D, Yu T, Xia Y, Zang B, Yan G, Qin C, Wu Q, Chen H (2020) Catalyzer: sub-millisecond startup for serverless computing with initialization-less booting. In: Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, pp 467–481

  88. Bermbach D, Karakaya AS, Buchholz S (2020) Using application knowledge to reduce cold starts in FaaS services. In: Proceedings of the 35th Annual ACM Symposium on Applied Computing, pp 134–143

  89. Mahajan K, Mahajan S, Misra V, Rubenstein D (2019) Exploiting content similarity to address cold start in container deployments. In: Proceedings of the 15th International Conference on emerging Networking EXperiments and Technologies, pp 37–39

  90. Bardsley D, Ryan L, Howard J (2018) Serverless performance and optimization strategies. In: 2018 IEEE International Conference on Smart Cloud (SmartCloud), IEEE, pp 19–26

  91. Mahmoudi N, Lin C, Khazaei H, Litoiu M (2019) Optimizing serverless computing: introducing an adaptive function placement algorithm. In: Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering, pp 203–213

  92. Aske A, Zhao X (2018) Supporting multi-provider serverless computing on the edge. In: Proceedings of the 47th International Conference on Parallel Processing Companion, pp 1–6

  93. Correia J, Ribeiro F, Filipe R, Arauio F, Cardoso J (2018) Response time characterization of microservice-based systems. In: 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), IEEE, pp 1–5

  94. Tsai PH, Hong HJ, Cheng AC, Hsu CH (2017) Distributed analytics in fog computing platforms using tensorflow and kubernetes. In: 2017 19th Asia-Pacific Network Operations and Management Symposium (APNOMS), IEEE, pp 145–150

  95. Zhou J, Velichkevich A, Prosvirov K, Garg A, Oshima Y, Dutta D (2019) Katib: A distributed general automl platform on kubernetes. In: 2019 \(\{\)USENIX\(\}\) Conference on Operational Machine Learning (OpML 19), pp 55–57

  96. Trakadas P, Nomikos N, Michailidis ET, Zahariadis T, Facca FM, Breitgand D, Rizou S, Masip X, Gkonis P (2019) Hybrid clouds for data-intensive, 5G-enabled IoT applications: an overview, key issues and relevant architecture. Sensors 19(16):3591

    Article  Google Scholar 

  97. Alonso-Monsalve S, García-Carballeira F, Calderón A (2018) A heterogeneous mobile cloud computing model for hybrid clouds. Future Gener Comput Syst 87:651–666

    Article  Google Scholar 

  98. Barcelona-Pons D, García-López P, Ruiz Á, Gómez-Gómez A, París G, Sánchez-Artigas M (2019) Faas orchestration of parallel workloads. In: Proceedings of the 5th International Workshop on Serverless Computing, pp 25–30

  99. Ramon-Cortes C, Serven A, Ejarque J, Lezzi D, Badia RM (2018) Transparent orchestration of task-based parallel applications in containers platforms. J Grid Comput 16(1):137–160

    Article  Google Scholar 

Download references

Author information

Author notes

  1. Subrota Kumar Mondal and Rui Pan have contributed equally.

Authors and Affiliations

  1. Faculty of Information Technology, Macau University of Science and Technology, Macao, China

    Subrota Kumar Mondal

  2. Faculty of Science, Vrije Universiteit Amsterdam, North Holland, Netherlands

    Rui Pan

  3. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia

    H M Dipu Kabir

  4. Faculty of Information Technology, Macau University of Science and Technology, Macao, China

    Tan Tian

  5. The Department of Computing Decision Sciences, Lingnan University, Hong Kong, China

    Hong-Ning Dai

Authors
  1. Subrota Kumar Mondal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H M Dipu Kabir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tan Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hong-Ning Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Subrota Kumar Mondal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mondal, S.K., Pan, R., Kabir, H.M.D. et al. Kubernetes in IT administration and serverless computing: An empirical study and research challenges. J Supercomput 78, 2937–2987 (2022). https://doi.org/10.1007/s11227-021-03982-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03982-3

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy