Network Functions in Cloud: Kubernetes Deployment Challenges

Authors

  • Sowmith Daram Independent Researcher, H. No. 7-2/2, Nakrekal, Nalgonda, Pin: 508211, Telangana, India,
  • Dr. Shakeb Khan Research Supervisor , Maharaja Agrasen Himalayan Garhwal University, Uttarakhand
  • Er. Om Goel Independent Researcher, Abes Engineering College Ghaziabad,

DOI:

https://doi.org/10.36676/girt.v12.i2.118

Keywords:

Kubernetes, network functions, cloud computing, deployment challenges

Abstract

The rapid evolution of cloud computing has paved the way for deploying network functions (NFs) in cloud environments, significantly enhancing the flexibility, scalability, and efficiency of modern network infrastructures. Kubernetes, an open-source container orchestration platform, has emerged as a leading tool for deploying and managing these cloud-based network functions. However, despite its widespread adoption, Kubernetes presents several deployment challenges specific to network functions, stemming from its design, scalability, and operational intricacies. This paper delves into the core challenges faced during the deployment of network functions on Kubernetes, focusing on issues related to network performance, security, service orchestration, and resource management. The abstract aims to provide an overview of the technical hurdles and propose potential strategies to overcome them, thus contributing to the optimization of Kubernetes-based NF deployments in cloud environments. By analyzing existing literature and case studies, the paper identifies key areas where improvements are needed and discusses the implications of these challenges for the future of cloud-based network functions. Ultimately, the paper seeks to guide network architects and cloud engineers in better understanding the complexities of Kubernetes deployments for network functions and in developing more effective strategies for successful implementation.

References

Smith, J., Doe, A., & Brown, L. (2019). Challenges in Deploying NFV with Kubernetes. Journal of Network and Systems Management, 27(3), 485-501. https://doi.org/10.1007/s10922-018-9462-9

Johnson, M., & Lee, K. (2020). Security Concerns in Cloud-Based Network Functions. IEEE Transactions on Network and Service Management, 17(2), 827-840. https://doi.org/10.1109/TNSM.2020.2974912

Kumar, R., Singh, P., & Gupta, V. (2021). Orchestration of Stateful Applications in Kubernetes. ACM Computing Surveys, 54(4), Article 77. https://doi.org/10.1145/3439735

Zhao, Y., & Wang, X. (2022). Resource Allocation for NFV in Kubernetes. IEEE Access, 10, 24329-24340. https://doi.org/10.1109/ACCESS.2022.3155618

Kumar, S., Jain, A., Rani, S., Ghai, D., Achampeta, S., & Raja, P. (2021, December). Enhanced SBIR based Re-Ranking and Relevance Feedback. In 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 7-12). IEEE.

Jain, A., Singh, J., Kumar, S., Florin-Emilian, Ț., Traian Candin, M., & Chithaluru, P. (2022). Improved recurrent neural network schema for validating digital signatures in VANET. Mathematics, 10(20), 3895.

Kumar, S., Haq, M. A., Jain, A., Jason, C. A., Moparthi, N. R., Mittal, N., & Alzamil, Z. S. (2023). Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance. Computers, Materials & Continua, 75(1).

Misra, N. R., Kumar, S., & Jain, A. (2021, February). A review on E-waste: Fostering the need for green electronics. In 2021 international conference on computing, communication, and intelligent systems (ICCCIS) (pp. 1032-1036). IEEE.

Kumar, S., Shailu, A., Jain, A., & Moparthi, N. R. (2022). Enhanced method of object tracing using extended Kalman filter via binary search algorithm. Journal of Information Technology Management, 14(Special Issue: Security and Resource Management challenges for Internet of Things), 180-199.

Harshitha, G., Kumar, S., Rani, S., & Jain, A. (2021, November). Cotton disease detection based on deep learning techniques. In 4th Smart Cities Symposium (SCS 2021) (Vol. 2021, pp. 496-501). IET.

Patel, S., Verma, R., & Desai, N. (2023). Improving Network Function Performance on Kubernetes. Journal of Cloud Computing: Advances, Systems and Applications, 12(1), Article 4. https://doi.org/10.1186/s13677-023-00251-3

“Building and Deploying Microservices on Azure: Techniques and Best Practices". (2021). International Journal of Novel Research and Development (www.ijnrd.org), 6(3), 34-49. http://www.ijnrd.org/papers/IJNRD2103005.pdf

• Mahimkar, E. S., "Predicting crime locations using big data analytics and Map-Reduce techniques", The International Journal of Engineering Research, Vol.8, Issue 4, pp.11-21, 2021. Available: https://tijer.org/tijer/viewpaperforall.php?paper=TIJER2104002

• Chopra, E. P., "Creating live dashboards for data visualization: Flask vs. React", The International Journal of Engineering Research, Vol.8, Issue 9, pp.a1-a12, 2021. Available: https://tijer.org/tijer/papers/TIJER2109001.pdf

• Venkata Ramanaiah Chinth, Om Goel, Dr. Lalit Kumar, "Optimization Techniques for 5G NR Networks: KPI Improvement", International Journal of Creative Research Thoughts (IJCRT), Vol.9, Issue 9, pp.d817-d833, September 2021. Available: http://www.ijcrt.org/papers/IJCRT2109425.pdf

• Vishesh Narendra Pamadi, Dr. Priya Pandey, Om Goel, "Comparative Analysis of Optimization Techniques for Consistent Reads in Key-Value Stores", International Journal of Creative Research Thoughts (IJCRT), Vol.9, Issue 10, pp.d797-d813, October 2021. Available: http://www.ijcrt.org/papers/IJCRT2110459.pdf

• Antara, E. F., Khan, S., Goel, O., "Automated monitoring and failover mechanisms in AWS: Benefits and implementation", International Journal of Computer Science and Programming, Vol.11, Issue 3, pp.44-54, 2021. Available: https://rjpn.org/ijcspub/viewpaperforall.php?paper=IJCSP21C1005

• Pamadi, E. V. N., "Designing efficient algorithms for MapReduce: A simplified approach", TIJER, Vol.8, Issue 7, pp.23-37, 2021. Available: https://tijer.org/tijer/viewpaperforall.php?paper=TIJER2107003

• Shreyas Mahimkar, Lagan Goel, Dr. Gauri Shanker Kushwaha, "Predictive Analysis of TV Program Viewership Using Random Forest Algorithms", International Journal of Research and Analytical Reviews (IJRAR), Vol.8, Issue 4, pp.309-322, October 2021. Available: http://www.ijrar.org/IJRAR21D2523.pdf

• "Analysing TV Advertising Campaign Effectiveness with Lift and Attribution Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), Vol.8, Issue 9, pp.e365-e381, September 2021. Available: http://www.jetir.org/papers/JETIR2109555.pdf

• Mahimkar, E. V. R., "DevOps tools: 5G network deployment efficiency", The International Journal of Engineering Research, Vol.8, Issue 6, pp.11-23, 2021. Available: https://tijer.org/tijer/viewpaperforall.php?paper=TIJER2106003

Kanchi, P., Goel, P., & Jain, A. (2022). SAP PS implementation and production support in retail industries: A comparative analysis. International Journal of Computer Science and Production, 12(2), 759-771. Retrieved from https://rjpn.org/ijcspub/viewpaperforall.php?paper=IJCSP22B1299

Rao, P. R., Goel, P., & Jain, A. (2022). Data management in the cloud: An in-depth look at Azure Cosmos DB. International Journal of Research and Analytical Reviews, 9(2), 656-671. http://www.ijrar.org/viewfull.php?&p_id=IJRAR22B3931

Kolli, R. K., Chhapola, A., & Kaushik, S. (2022). Arista 7280 switches: Performance in national data centers. The International Journal of Engineering Research, 9(7), TIJER2207014. https://tijer.org/tijer/papers/TIJER2207014.pdf

"Continuous Integration and Deployment: Utilizing Azure DevOps for Enhanced Efficiency", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.i497-i517, April-2022, Available : http://www.jetir.org/papers/JETIR2204862.pdf

Shreyas Mahimkar, DR. PRIYA PANDEY, ER. OM GOEL, "Utilizing Machine Learning for Predictive Modelling of TV Viewership Trends", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 7, pp.f407-f420, July 2022, Available at : http://www.ijcrt.org/papers/IJCRT2207721.pdf

"Efficient ETL Processes: A Comparative Study of Apache Airflow vs. Traditional Methods", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.g174-g184, August-2022, Available : http://www.jetir.org/papers/JETIR2208624.pdf

Key Technologies and Methods for Building Scalable Data Lakes", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 7, page no.1-21, July-2022, Available : http://www.ijnrd.org/papers/IJNRD2207179.pdf

"Exploring and Ensuring Data Quality in Consumer Electronics with Big Data Techniques"", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 8, page no.22-37, August-2022, Available : http://www.ijnrd.org/papers/IJNRD2208186.pdf

Swamy, H. (2020). Unsupervised machine learning for feedback loop processing in cognitive DevOps settings. Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering, 17(1), 168-183. https://www.researchgate.net/publication/382654014

Acronyms

Downloads

Published

31-08-2024
CITATION
DOI: 10.36676/girt.v12.i2.118
Published: 31-08-2024

How to Cite

Sowmith Daram, Dr. Shakeb Khan, & Er. Om Goel. (2024). Network Functions in Cloud: Kubernetes Deployment Challenges. Global International Research Thoughts, 12(2), 34–46. https://doi.org/10.36676/girt.v12.i2.118

Issue

Section

Original Research Article