Comparative Analysis of Container Orchestration Platforms: Kubernetes vs. Docker Swarm

Authors

  • Venkat Marella Independent Researcher, USA Author

DOI:

https://doi.org/10.32628/IJSRST24105254

Keywords:

Microservices, Docker Swarm, Kubernetes, Web Application, Flexibility, Granularity, DevOps Methodologies, Container Orchestrators, Resource Management (RM)

Abstract

Novel software architecture patterns, including microservices, have surfaced in the last ten years to increase the modularity of applications and to simplify their development, testing, scaling, and component replacement. In response to these emerging trends, new approaches such as DevOps methods and technologies have arisen to facilitate automation and monitoring across the whole software construction lifecycle, fostering improved collaboration between software development and operations teams. The resource management (RM) strategies of Kubernetes and Docker Swarm, two well-known container orchestration technologies, are compared in this article. The main distinctions between RM, scheduling, and scalability are examined, with an emphasis on Kubernetes' flexibility and granularity in contrast to Docker Swarm's simplicity and use. In this article, a case study comparing the performance of two popular container orchestrators—Kubernetes and Docker Swarm—over a Web application built using the microservices architecture is presented. By raising the number of users, we compare how well Docker Swarm and Kubernetes perform under stress. This study aims to provide academics and practitioners with an understanding of how well Docker Swarm and Kubernetes function in systems built using the suggested microservice architecture. The authors' Web application is a kind of loyalty program, meaning that it offers a free item upon reaching a certain quantity of purchases. According to the study's findings, Docker Swarm outperforms Kubernetes in terms of efficiency as user counts rise.

Downloads

Download data is not yet available.

References

Pshychenko, D. (2024). Evaluation of the effectiveness of implementing AI-based CRM systems. Innovacionnaja nauka, (7-2), 40-45.

Balla, D., Simon, C., & Maliosz, M. (2020, April). Adaptive scaling of Kubernetes pods. In NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/NOMS47738.2020.9110428

Singh, N., Hamid, Y., Juneja, S., Srivastava, G., Dhiman, G., Gadekallu, T. R., & Shah, M. A. (2023). Load balancing and service discovery using Docker Swarm for microservice based big data applications. Journal of Cloud Computing, 12(1), 4. DOI: https://doi.org/10.1186/s13677-022-00358-7

Soltesz, S.; Soltesz, S.; Pötzl, H.; Pötzl, H.; Fiuczynski, M.E.; Fiuczynski, M.E.; Bavier, A.; Bavier, A.; Peterson, L.; Peterson, L. Container-based operating system virtualization: A scalable, high-performance alternative to hypervisors. SIGOPS Oper. Syst. Rev. 2007, 41, 275–287. DOI: https://doi.org/10.1145/1272998.1273025

Xavier, M.G.; Neves, M.V.; Rossi, F.D.; Ferreto, T.C.; Lange, T.; de Rose, C.F. Performance Evaluation of Container-based Virtualization for High Performance Computing Environments. In Proceedings of the 2013 21st Euromicro International Conference Parallel, Distributed, Network-Based Processing, Belfast, UK, 27 February–1 March 2013; pp. 233–240. DOI: https://doi.org/10.1109/PDP.2013.41

Dua, R.; Raja, A.R.; Kakadia, D. Virtualization vs Containerization to Support PaaS. In Proceedings of the 2014 IEEE International Conference on Cloud Engineering, Boston, MA, USA, 11–14 March 2014; pp. 610–614. DOI: https://doi.org/10.1109/IC2E.2014.41

Felter, W.; Ferreira, A.; Rajamony, R.; Rubio, J. An updated performance comparison of virtual machines and Linux containers. In Proceedings of the 2015 IEEE international symposium on performance analysis of systems and software (ISPASS), Philadelphia, PA, USA, 29–31 March 2015; pp. 171–172. DOI: https://doi.org/10.1109/ISPASS.2015.7095802

Tosatto, A.; Ruiu, P.; Attanasio, A. Container-Based Orchestration in Cloud: State of the Art and Challenges. In Proceedings of the 2015 Ninth International Conference on Complex, Intelligent, and Software Intensive Systems, Blumenau, Brazil, 8–10 July 2015; pp. 70–75. DOI: https://doi.org/10.1109/CISIS.2015.35

Yang Zhao et al., “Performance of Container Networking Technologies”, in Proceedings of the Workshop on Hot Topics in Container Networking and Networked Systems (HotConNet '17), pp.1-6.

Z. Nikdel et al., “DockerSim: Full-stack simulation of container-based Software-as-a-Service (SaaS) cloud deployments and environments”, in 2017 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), 2017, pp. 1-6. DOI: https://doi.org/10.1109/PACRIM.2017.8121898

R. Morabito, “A performance evaluation of container technologies on Internet of Things devices”, in 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 999-1000. DOI: https://doi.org/10.1109/INFCOMW.2016.7562228

Malviya, A., & Dwivedi, R. K. (2022, March). A comparative analysis of container orchestration tools in cloud computing. In 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 698-703). IEEE. DOI: https://doi.org/10.23919/INDIACom54597.2022.9763171

Fayos-Jordan, R., Felici-Castell, S., Segura-Garcia, J., Lopez-Ballester, J., & Cobos, M. (2020). Performance comparison of container orchestration platforms with low cost devices in the fog, assisting Internet of Things applications. Journal of Network and Computer Applications, 169, 102788. DOI: https://doi.org/10.1016/j.jnca.2020.102788

Kumar, E. S., Ramamoorthy, R., Kesavan, S., Shobha, T., Patil, S., & Vighneshwari, B. (2024, February). Comparative Study and Analysis of Cloud Container Technology. In 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 1681-1686). IEEE. DOI: https://doi.org/10.23919/INDIACom61295.2024.10499108

Purahong, B., Sithiyopasakul, J., Sithiyopasakul, P., Lasakul, A., & Benjangkaprasert, C. (2023). Automated Resource Management System Based upon Container Orchestration Tools Comparison. Journal of Advances in Information Technology, 14(3). DOI: https://doi.org/10.12720/jait.14.3.501-509

Vasireddy, I., Ramya, G., & Kandi, P. (2023). Kubernetes and Docker Load Balancing: State-of-the-Art Techniques and Challenges. International Journal of Innovative Research in Engineering & Management, 10(6), 49-54. DOI: https://doi.org/10.55524/ijirem.2023.10.6.7

Carrión, C. (2022). Kubernetes as a standard container orchestrator-a bibliometric analysis. Journal of Grid Computing, 20(4), 42. DOI: https://doi.org/10.1007/s10723-022-09629-8

Vincent Reniers, “The Prospects for Multi-Cloud Deployment of SaaS Applications with Container Orchestration Platforms”, Middleware Doctoral Symposium'16, article 5, 2 pages.

Wito Delnat et al., “K8-scalar: a workbench to compare autoscalers for container-orchestrated database clusters”, in Proceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS '18), pp. 33-39. DOI: https://doi.org/10.1145/3194133.3194162

M. Amaral et al., “Performance evaluation of microservices architectures using containers,” in 2015 IEEE 14th International Symposium on Network Computing and Applications, 2015, pp. 27–34. DOI: https://doi.org/10.1109/NCA.2015.49

L. Mercl and J. Pavlik, “The comparison of container orchestrators,” in Third International Congress on Information and Communication Technology, X.-S. Yang, S. Sherratt, N. Dey, and A. Joshi, Eds. Singapore: Springer Singapore, 2019, pp. 677–685. DOI: https://doi.org/10.1007/978-981-13-1165-9_62

Y. Pan, I. Chen, F. Brasileiro, G. Jayaputera, and R. Sinnott, “A performance comparison of cloud-based container orchestration tools,” in 2019 IEEE International Conference on Big Knowledge (ICBK), Nov 2019, pp. 191–198. DOI: https://doi.org/10.1109/ICBK.2019.00033

W. Li and A. Kanso, “Comparing containers versus virtual machines for achieving high availability,” in 2015 IEEE International Conference on Cloud Engineering, 2015, pp. 353–358. DOI: https://doi.org/10.1109/IC2E.2015.79

G. C. Fox et al., “Real time streaming data grid applications,” in Distributed Cooperative Laboratories: Networking, Instrumentation, and Measurements. Springer, 2006, pp. 253–267. DOI: https://doi.org/10.1007/0-387-30394-4_17

M. Aktas et al., “iservo: Implementing the international solid earth research virtual observatory by integrating computational grid and geographical information web services,” in Computational Earthquake Physics: Simulations, Analysis and Infrastructure, Part II. Springer, 2006, pp. 2281–2296. DOI: https://doi.org/10.1007/978-3-7643-8131-8_3

Cherukuri, H., Goel, E. L., & Kushwaha, G. S. (2021). Monetizing financial data analytics: Best practice. International Journal of Computer Science and Publication (IJCSPub), 11(1), 76-87.

Chaturvedi, R., Sharma, S., & Narne, S. (2023). Advanced Big Data Mining Techniques for Early Detection of Heart Attacks in Clinical Data. Journal for Research in Applied Sciences and Biotechnology, 2(3), 305–316. https://doi.org/10.55544/jrasb.2.3.38

Chaturvedi, R., Sharma, S., & Narne, S. (2023). Advanced Big Data Mining Techniques for Early Detection of Heart Attacks in Clinical Data. Journal for Research in Applied Sciences and Biotechnology, 2(3), 305–316. https://doi.org/10.55544/jrasb.2.3.38 DOI: https://doi.org/10.55544/jrasb.2.3.38

Chaturvedi, R., Sharma, S., & Narne, S. (2023). Harnessing Data Mining for Early Detection and Prognosis of Cancer: Techniques and Challenges. Journal for Research in Applied Sciences and Biotechnology, 2(1), 282–293. https://doi.org/10.55544/jrasb.2.1.42 DOI: https://doi.org/10.55544/jrasb.2.1.42

Mehra, A. (2023). Strategies for scaling EdTech startups in emerging markets. International Journal of Communication Networks and Information Security, 15(1), 259-274. Available online at https://ijcnis.org

Mehra, A. (2021). The impact of public-private partnerships on global educational platforms. Journal of Informatics Education and Research, 1(3), 9-28. Retrieved from http://jier.org

Ankur Mehra. (2019). Driving Growth in the Creator Economy through Strategic Content Partnerships. International Journal for Research Publication and Seminar, 10(2), 118–135. https://doi.org/10.36676/jrps.v10.i2.1519 DOI: https://doi.org/10.36676/jrps.v10.i2.1519

Ankur Mehra. (2023). Web3 and EdTech startups’ Market Expansion in APAC. International Journal of Research Radicals in Multidisciplinary Fields, ISSN: 2960-043X, 2(2), 94–118. Retrieved from https://www.researchradicals.com/index.php/rr/article/view/117

Mehra, A. (2023). Leveraging Data-Driven Insights to Enhance Market Share in the Media Industry. Journal for Research in Applied Sciences and Biotechnology, 2(3), 291–304. https://doi.org/10.55544/jrasb.2.3.37 DOI: https://doi.org/10.55544/jrasb.2.3.37

Ankur Mehra. (2022). Effective Team Management Strategies in Global Organizations. Universal Research Reports, 9(4), 409–425. https://doi.org/10.36676/urr.v9.i4.1363 DOI: https://doi.org/10.36676/urr.v9.i4.1363

Mehra, A. (2023). Innovation in brand collaborations for digital media platforms. IJFANS: International Journal of Food and Nutritional Sciences, 12(6), 231–250.

Ankur Mehra. (2022). The Role of Strategic Alliances in the Growth of the Creator Economy. European Economic Letters (EEL), 12(1). Retrieved from https://www.eelet.org.uk/index.php/journal/article/view/1925

Swethasri Kavuri. (2022). Optimizing Data Refresh Mechanisms for Large-Scale Data Warehouses. International Journal of Communication Networks and Information Security (IJCNIS), 14(2), 285–305. Retrieved from https://www.ijcnis.org/index.php/ijcnis/article/view/7413

Swethasri Kavuri, Suman Narne, " Implementing Effective SLO Monitoring in High-Volume Data Processing Systems, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 2, pp.558-578, March-April-2020. Available at doi : https://doi.org/10.32628/CSEIT206479 DOI: https://doi.org/10.32628/CSEIT206479

Swethasri Kavuri, Suman Narne, " Improving Performance of Data Extracts Using Window-Based Refresh Strategies, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 5, pp.359-377, September-October-2021. Available at doi : https://doi.org/10.32628/IJSRSET2310631 DOI: https://doi.org/10.32628/IJSRSET2310631

Swethasri Kavuri, " Automation in Distributed Shared Memory Testing for Multi-Processor Systems, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 6, Issue 3, pp.508-521, May-June-2019. Available at doi : https://doi.org/10.32628/IJSRSET12411594 DOI: https://doi.org/10.32628/IJSRSET12411594

Swethasri Kavuri, " Advanced Debugging Techniques for Multi-Processor Communication in 5G Systems, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 5, pp.360-384, September-October-2023. Available at doi : https://doi.org/10.32628/CSEIT239071 DOI: https://doi.org/10.32628/CSEIT239071

Shivarudra, A. (2021). Enhancing automation testing strategies for core banking applications. International Journal of All Research Education and Scientific Methods (IJARESM), 9(12), 1. Available online at http://www.ijaresm.com

Ashwini Shivarudra. (2023). Best Practices for Testing Payment Systems: A Focus on SWIFT, SEPA, and FED ISO Formats. International Journal of Communication Networks and Information Security (IJCNIS), 15(3), 330–344. Retrieved from https://www.ijcnis.org/index.php/ijcnis/article/view/7519

Shivarudra, A. (2019). Leveraging TOSCA and Selenium for efficient test automation in financial services. International Journal of All Research Education and Scientific Methods (IJARESM), 7(10), 56–64.

Shivarudra, A. (2021). The Role of Automation in Reducing Testing Time for Banking Systems. Integrated Journal for Research in Arts and Humanities, 1(1), 83–89. https://doi.org/10.55544/ijrah.1.1.12 DOI: https://doi.org/10.55544/ijrah.1.1.12

Ashwini Shivarudra. (2022). Advanced Techniques in End-to-End Testing of Core Banking Solutions. International Journal of Research Radicals in Multidisciplinary Fields, ISSN: 2960-043X, 1(2), 112–124. Retrieved from https://www.researchradicals.com/index.php/rr/article/view/121

Shivarudra, A. (2022). Implementing Agile Testing Methodologies in Banking Software Project. Journal for Research in Applied Sciences and Biotechnology, 1(4), 215–225. https://doi.org/10.55544/jrasb.1.4.32 DOI: https://doi.org/10.55544/jrasb.1.4.32

Bhatt, S. (2021). Optimizing SAP Migration Strategies to AWS: Best Practices and Lessons Learned. Integrated Journal for Research in Arts and Humanities, 1(1), 74–82. https://doi.org/10.55544/ijrah.1.1.11 DOI: https://doi.org/10.55544/ijrah.1.1.11

Bhatt, S. (2022). Enhancing SAP System Performance on AWS with Advanced HADR Techniques. Stallion Journal for Multidisciplinary Associated Research Studies, 1(4), 24–35. https://doi.org/10.55544/sjmars.1.4.6

Bhatt, S., & Narne, S. (2023). Streamlining OS/DB Migrations for SAP Environments: A Comparative Analysis of Tools and Methods. Stallion Journal for Multidisciplinary Associated Research Studies, 2(4), 14–27. https://doi.org/10.55544/sjmars.2.4.3

Bhatt, S. (2023). Implementing SAP S/4HANA on AWS: Challenges and solutions for large enterprises. International Journal of Computer Science and Mobile Computing, 12(10), 71–88.

https://doi.org/10.47760/ijcsmc.2023.v12i10.007

Sachin Bhatt , " Innovations in SAP Landscape Optimization Using Cloud-Based Architectures, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 2, pp.579-590, March-April-2020. DOI: https://doi.org/10.32628/CSEIT206480

Bhatt, S. (2022). Leveraging AWS tools for high availability and disaster recovery in SAP applications. International Journal of Scientific Research in Science, Engineering and Technology, 9(2), 482–496. https://doi.org/10.32628/IJSRSET2072122 DOI: https://doi.org/10.32628/IJSRSET2072122

Bhatt, S. (2021). A comprehensive guide to SAP data center migrations: Techniques and case studies. International Journal of Scientific Research in Science, Engineering and Technology, 8(5), 346–358. https://doi.org/10.32628/IJSRSET2310630

Bhatt, S. (2023). Integrating Non-SAP Systems with SAP Environments on AWS: Strategies for Seamless Operations. Journal for Research in Applied Sciences and Biotechnology, 2(6), 292–305. https://doi.org/10.55544/jrasb.2.6.41 DOI: https://doi.org/10.55544/jrasb.2.6.41

Paulraj, B. (2023). Enhancing Data Engineering Frameworks for Scalable Real-Time Marketing Solutions. Integrated Journal for Research in Arts and Humanities, 3(5), 309–315. https://doi.org/10.55544/ijrah.3.5.34 DOI: https://doi.org/10.55544/ijrah.3.5.34

Paulraj, B. (2023). Optimizing telemetry data processing pipelines for large-scale gaming platforms. International Journal of Scientific Research in Science, Engineering and Technology, 9(1), 401. https://doi.org/10.32628/IJSRSET23103132 DOI: https://doi.org/10.32628/IJSRSET23103132

Paulraj, B. (2022). Building Resilient Data Ingestion Pipelines for Third-Party Vendor Data Integration. Journal for Research in Applied Sciences and Biotechnology, 1(1), 97–104. https://doi.org/10.55544/jrasb.1.1.14 DOI: https://doi.org/10.55544/jrasb.1.1.14

Paulraj, B. (2022). The Role of Data Engineering in Facilitating Ps5 Launch Success: A Case Study. International Journal on Recent and Innovation Trends in Computing and Communication, 10(11), 219–225. https://doi.org/10.17762/ijritcc.v10i11.11145 DOI: https://doi.org/10.17762/ijritcc.v10i11.11145

Balachandar Paulraj. (2021). Implementing Feature and Metric Stores for Machine Learning Models in the Gaming Industry. European Economic Letters (EEL), 11(1). Retrieved from https://www.eelet.org.uk/index.php/journal/article/view/1924

Balachandar Paulraj. (2023). Data-Driven Decision Making in Gaming Platforms: Metrics and Strategies. International Journal of Research Radicals in Multidisciplinary Fields, ISSN: 2960-043X, 2(2), 81–93. Retrieved from https://www.researchradicals.com/index.php/rr/article/view/116

Alok Gupta. (2021). Reducing Bias in Predictive Models Serving Analytics Users: Novel Approaches and their Implications. International Journal on Recent and Innovation Trends in Computing and Communication, 9(11), 23–30. Retrieved from https://ijritcc.org/index.php/ijritcc/article/view/11108

Gupta, A., Selvaraj, P., Singh, R. K., Vaidya, H., & Nayani, A. R. (2022). The Role of Managed ETL Platforms in Reducing Data Integration Time and Improving User Satisfaction. Journal for Research in Applied Sciences and Biotechnology, 1(1), 83–92. https://doi.org/10.55544/jrasb.1.1.12 DOI: https://doi.org/10.55544/jrasb.1.1.12

Selvaraj, P. . (2022). Library Management System Integrating Servlets and Applets Using SQL Library Management System Integrating Servlets and Applets Using SQL database. International Journal on Recent and Innovation Trends in Computing and Communication, 10(4), 82–89. https://doi.org/10.17762/ijritcc.v10i4.11109 DOI: https://doi.org/10.17762/ijritcc.v10i4.11109

Vaidya, H., Nayani, A. R., Gupta, A., Selvaraj, P., & Singh, R. K. (2020). Effectiveness and future trends of cloud computing platforms. Tuijin Jishu/Journal of Propulsion Technology, 41(3). https://doi.org/10.52783/tjjpt.v45.i03.7820

Harsh Vaidya, Aravind Reddy Nayani, Alok Gupta, Prassanna Selvaraj, & Ravi Kumar Singh. (2023). Using OOP Concepts for the Development of a Web-Based Online Bookstore System with a Real-Time Database. International Journal for Research Publication and Seminar, 14(5), 253–274. https://doi.org/10.36676/jrps.v14.i5.1502 DOI: https://doi.org/10.36676/jrps.v14.i5.1502

Aravind Reddy Nayani, Alok Gupta, Prassanna Selvaraj, Ravi Kumar Singh, & Harsh Vaidya. (2019). Search and Recommendation Procedure with the Help of Artificial Intelligence. International Journal for Research Publication and Seminar, 10(4), 148–166. https://doi.org/10.36676/jrps.v10.i4.1503 DOI: https://doi.org/10.36676/jrps.v10.i4.1503

Aravind Reddy Nayani, Alok Gupta, Prassanna Selvaraj, Ravi Kumar Singh, Harsh Vaidya. (2023). Online Bank Management System in Eclipse IDE: A Comprehensive Technical Study. European Economic Letters (EEL), 13(3), 2095–2113. Retrieved from https://www.eelet.org.uk/index.php/journal/article/view/1874

Sagar Shukla. (2021). Integrating Data Analytics Platforms with Machine Learning Workflows: Enhancing Predictive Capability and Revenue Growth. International Journal on Recent and Innovation Trends in Computing and Communication, 9(12), 63–74. Retrieved from https://ijritcc.org/index.php/ijritcc/article/view/11119

Sneha Aravind. (2021). Integrating REST APIs in Single Page Applications using Angular and TypeScript. International Journal of Intelligent Systems and Applications in Engineering, 9(2), 81 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6829

Sachin Bhatt , " A Comprehensive Guide to SAP Data Center Migrations: Techniques and Case Studies, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 5, pp.346-358, September-October-2021. Available at doi : https://doi.org/10.32628/IJSRSET2310630

Bhatt, S. (2021). A comprehensive guide to SAP data center migrations: Techniques and case studies. International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), 8(5), 346–358. https://doi.org/10.32628/IJSRSET2310630 DOI: https://doi.org/10.32628/IJSRSET2310630

Bhatt, S. (2023). Implementing SAP S/4HANA on AWS: Challenges and solutions for large enterprises. International Journal of Computer Science and Mobile Computing, 12(10), 71–88. DOI: https://doi.org/10.47760/ijcsmc.2023.v12i10.007

Rinkesh Gajera , "Leveraging Procore for Improved Collaboration and Communication in Multi-Stakeholder Construction Projects", International Journal of Scientific Research in Civil Engineering (IJSRCE), ISSN : 2456-6667, Volume 3, Issue 3, pp.47-51, May-June.2019 DOI: https://doi.org/10.32628/IJSRCE19338

Rinkesh Gajera , "Integrating Power Bi with Project Control Systems: Enhancing Real-Time Cost Tracking and Visualization in Construction", International Journal of Scientific Research in Civil Engineering (IJSRCE), ISSN : 2456-6667, Volume 7, Issue 5, pp.154-160, September-October.2023 DOI: https://doi.org/10.32628/IJSRCE123761

URL : https://ijsrce.com/IJSRCE123761

Rinkesh Gajera, 2023. Developing a Hybrid Approach: Combining Traditional and Agile Project Management Methodologies in Construction Using Modern Software Tools, ESP Journal of Engineering & Technology Advancements 3(3): 78-83.

Gajera, R. (2023). Evaluating the effectiveness of earned value management (EVM) implementation using integrated project control software suites. Journal of Computational Analysis and Applications, 31(4), 654-658.

Saoji, R., Nuguri, S., Shiva, K., Etikani, P., & Bhaskar, V. V. S. R. (2019). Secure federated learning framework for distributed AI model training in cloud environments. International Journal of Open Publication and Exploration (IJOPE), 7(1), 31. Available online at https://ijope.com.

Savita Nuguri, Rahul Saoji, Krishnateja Shiva, Pradeep Etikani, & Vijaya Venkata Sri Rama Bhaskar. (2021). OPTIMIZING AI MODEL DEPLOYMENT IN CLOUD ENVIRONMENTS: CHALLENGES AND SOLUTIONS. International Journal for Research Publication and Seminar, 12(2), 159–168. https://doi.org/10.36676/jrps.v12.i2.1461 DOI: https://doi.org/10.36676/jrps.v12.i2.1461

Kaur, J., Choppadandi, A., Chenchala, P. K., Nuguri, S., & Saoji, R. (2022). Machine learning-driven IoT systems for precision agriculture: Enhancing decision-making and efficiency. Webology, 19(6), 2158. Retrieved from http://www.webology.org.

Lohith Paripati, Varun Nakra, Pandi Kirupa Gopalakrishna Pandian, Rahul Saoji, Bhanu Devaguptapu. (2023). Exploring the Potential of Learning in Credit Scoring Models for Alternative Lending Platforms. European Economic Letters (EEL), 13(4), 1331–1241. https://doi.org/10.52783/eel.v13i4.179.

Etikani, P., Bhaskar, V. V. S. R., Nuguri, S., Saoji, R., & Shiva, K. (2023). Automating machine learning workflows with cloud-based pipelines. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 375–382. https://doi.org/10.48047/ijisae.2023.11.1.37

Etikani, P., Bhaskar, V. V. S. R., Palavesh, S., Saoji, R., & Shiva, K. (2023). AI-powered algorithmic trading strategies in the stock market. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 264–277. https://doi.org/10.1234/ijsdip.org_2023-Volume-11-Issue-1_Page_264-277.

Saoji, R., Nuguri, S., Shiva, K., Etikani, P., & Bhaskar, V. V. S. R. (2021). Adaptive AI-based deep learning models for dynamic control in software-defined networks. International Journal of Electrical and Electronics Engineering (IJEEE), 10(1), 89–100. ISSN (P): 2278–9944; ISSN (E): 2278–9952

Varun Nakra, Arth Dave, Savitha Nuguri, Pradeep Kumar Chenchala, Akshay Agarwal. (2023). Robo-Advisors in Wealth Management: Exploring the Role of AI and ML in Financial Planning. European Economic Letters (EEL), 13(5), 2028–2039. Retrieved from https://www.eelet.org.uk/index.php/journal/article/view/1514.

Chinta, U., & Goel, P. (2022). Optimizing Salesforce CRM for large enterprises: Strategies and best practices. International Journal of Creative Research Thoughts (IJCRT), 9(5), 282. https://doi.org/10.36676/irt DOI: https://doi.org/10.36676/irt

Mahadik, S., Chinta, U., Bhimanapati, V. B. R., Goel, P., & Jain, A. (2023). Product roadmap planning in dynamic markets. Innovative Research Thoughts, 9(5), 282. https://doi.org/10.36676/irt DOI: https://doi.org/10.36676/irt.v9.i5.1488

Chinta, U., Aggarwal, A., & Jain, S. (2020). Risk management strategies in Salesforce project delivery: A case study approach. Innovative Research Thoughts, 7(3). DOI: https://doi.org/10.36676/irt.v7.i3.1452

Ghavate, N. (2018). An Computer Adaptive Testing Using Rule Based. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 4(I). Retrieved from http://asianssr.org/index.php/ajct/article/view/443

Shanbhag, R. R., Dasi, U., Singla, N., Balasubramanian, R., & Benadikar, S. (2020). Overview of cloud computing in the process control industry. International Journal of Computer Science and Mobile Computing, 9(10), 121-146. https://www.ijcsmc.com DOI: https://doi.org/10.47760/ijcsmc.2020.v09i10.016

Benadikar, S. (2021). Developing a scalable and efficient cloud-based framework for distributed machine learning. International Journal of Intelligent Systems and Applications in Engineering, 9(4), 288. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6761

Shanbhag, R. R., Benadikar, S., Dasi, U., Singla, N., & Balasubramanian, R. (2022). Security and privacy considerations in cloud-based big data analytics. Journal of Propulsion Technology, 41(4), 62-81.

Shanbhag, R. R., Balasubramanian, R., Benadikar, S., Dasi, U., & Singla, N. (2021). Developing scalable and efficient cloud-based solutions for ecommerce platforms. International Journal of Computer Science and Engineering (IJCSE), 10(2), 39-58. http://www.iaset.us/archives?jname=14_2&year=2021&submit=Search

Shanbhag, R. R. (2023). Accountability frameworks for autonomous AI decision-making systems. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 565-569.

Ugandhar Dasi. (2024). Developing A Cloud-Based Natural Language Processing (NLP) Platform for Sentiment Analysis and Opinion Mining of Social Media Data. International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 165–174. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6406

Shanbhag, R. R., Benadikar, S., Dasi, U., Singla, N., & Balasubramanian, R. (2024). Investigating the application of transfer learning techniques in cloud-based AI systems for improved performance and reduced training time. Letters in High Energy Physics, 202431. https://lettersinhighenergyphysics.com/index.php/LHEP/article/view/551

Rishabh Rajesh Shanbhag, Rajkumar Balasubramanian, Ugandhar Dasi, Nikhil Singla, & Siddhant Benadikar. (2022). Case Studies and Best Practices in Cloud-Based Big Data Analytics for Process Control. International Journal for Research Publication and Seminar, 13(5), 292–311. https://doi.org/10.36676/jrps.v13.i5.1462 DOI: https://doi.org/10.36676/jrps.v13.i5.1462

https://jrps.shodhsagar.com/index.php/j/article/view/1462

Ugandhar Dasi, Nikhil Singla, Rajkumar Balasubramanian, Siddhant Benadikar, Rishabh Rajesh Shanbhag. (2024). Analyzing the Security and Privacy Challenges in Implementing Ai and Ml Models in Multi-Tenant Cloud Environments. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(2), 262–270. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/108

Nikhil Singla. (2023). Assessing the Performance and Cost-Efficiency of Serverless Computing for Deploying and Scaling AI and ML Workloads in the Cloud. International Journal of Intelligent Systems and Applications in Engineering, 11(5s), 618–630. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6730

Tripathi, A. (2020). AWS serverless messaging using SQS. IJIRAE: International Journal of Innovative Research in Advanced Engineering, 7(11), 391-393. DOI: https://doi.org/10.26562/ijirae.2020.v0711.003

Tripathi, A. (2019). Serverless architecture patterns: Deep dive into event-driven, microservices, and serverless APIs. International Journal of Creative Research Thoughts (IJCRT), 7(3), 234-239. Retrieved from http://www.ijcrt.org

Tripathi, A. (2023). Low-code/no-code development platforms. International Journal of Computer Applications (IJCA), 4(1), 27–35. Retrieved from https://iaeme.com/Home/issue/IJCA?Volume=4&Issue=1

Tripathi, A. (2024). Unleashing the power of serverless architectures in cloud technology: A comprehensive analysis and future trends. IJIRAE: International Journal of Innovative Research in Advanced Engineering, 11(03), 138-146. DOI: https://doi.org/10.26562/ijirae.2024.v1103.01

Tripathi, A. (2024). Enhancing Java serverless performance: Strategies for container warm-up and optimization. International Journal of Computer Engineering and Technology (IJCET), 15(1), 101-106.

Tripathi, A. (2022). Serverless deployment methodologies: Smooth transitions and improved reliability. IJIRAE: International Journal of Innovative Research in Advanced Engineering, 9(12), 510-514. DOI: https://doi.org/10.26562/ijirae.2022.v0912.10

Tripathi, A. (2022). Deep dive into Java tiered compilation: Performance optimization. International Journal of Creative Research Thoughts (IJCRT), 10(10), 479-483. Retrieved from https://www.ijcrt.org

Krishnateja Shiva. (2022). Leveraging Cloud Resource for Hyperparameter Tuning in Deep Learning Models. International Journal on Recent and Innovation Trends in Computing and Communication, 10(2), 30–35. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10980

Pradeep Etikani. (2023). Automating Machine Learning Workflows with Cloud-Based Pipelines. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 375 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6722

Vijaya Venkata Sri Rama Bhaskar, Akhil Mittal, Santosh Palavesh, Krishnateja Shiva, Pradeep Etikani. (2020). Regulating AI in Fintech: Balancing Innovation with Consumer Protection. European Economic Letters (EEL), 10(1). https://doi.org/10.52783/eel.v10i1.1810 DOI: https://doi.org/10.52783/eel.v10i1.1810

https://www.eelet.org.uk/index.php/journal/article/view/1810

Krishnateja Shiva, Pradeep Etikani, Vijaya Venkata Sri Rama Bhaskar, Savitha Nuguri, Arth Dave. (2024). Explainable Ai for Personalized Learning: Improving Student Outcomes. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(2), 198–207. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/100

Nitin Prasad. (2022). Security Challenges and Solutions in Cloud-Based Artificial Intelligence and Machine Learning Systems. International Journal on Recent and Innovation Trends in Computing and Communication, 10(12), 286–292. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10750

Jigar Shah , Joel lopes , Nitin Prasad , Narendra Narukulla , Venudhar Rao Hajari , Lohith Paripati. (2023). Optimizing Resource Allocation And Scalability In Cloud-Based Machine Learning Models. Migration Letters, 20(S12), 1823–1832. Retrieved from https://migrationletters.com/index.php/ml/article/view/10652

Big Data Analytics using Machine Learning Techniques on Cloud Platforms. (2019). International Journal of Business Management and Visuals, ISSN: 3006-2705, 2(2), 54-58. https://ijbmv.com/index.php/home/article/view/76

Lohith Paripati. (2024). Edge Computing for AI and ML: Enhancing Performance and Privacy in Data Analysis . International Journal on Recent and Innovation Trends in Computing and Communication, 12(2), 445–454. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10848

Arth Dave, Lohith Paripati, Narendra Narukulla, Venudhar Rao Hajari, & Akshay Agarwal. (2024). Cloud-Based Regulatory Intelligence Dashboards: Empowering Decision-Makers with Actionable Insights. Innovative Research Thoughts, 10(2), 43–50. Retrieved from https://irt.shodhsagar.com/index.php/j/article/view/1272

Narukulla, N., Lopes, J., Hajari, V. R., Prasad, N., & Swamy, H. (2021). Real Time Data Processing and Predictive Analytics Using Cloud Based Machine Learning. Tuijin Jishu/Journal of Propulsion Technology, 42(4), 91-102. https://www.propulsiontechjournal.com/index.php/journal/article/view/6757

Prasad, N., Narukulla, N., Hajari, V. R., Paripati, L., & Shah, J. (2020). AI-driven data governance framework for cloud-based data analytics. Volume, 17(2), 1551-1561.

https://www.webology.org/abstract.php?id=5212

Lohith Paripati, Venudhar Rao Hajari, Narendra Narukulla, Nitin Prasad, Jigar Shah, & Akshay Agarwal. (2024). Ethical Considerations in AI-Driven Predictive Analytics: Addressing Bias and Fairness Issues. Darpan International Research Analysis, 12(2), 34–50. Retrieved from https://dira.shodhsagar.com/index.php/j/article/view/40

Shah, J., Narukulla, N., Hajari, V. R., Paripati, L., & Prasad, N. (2021). Scalable machine learning infrastructure on cloud for large-scale data processing. Tuijin Jishu/Journal of Propulsion Technology, 42(2), 45-53. https://propulsiontechjournal.com/index.php/journal/article/view/7166 DOI: https://doi.org/10.52783/tjjpt.v42.i2.7166

Lohith Paripati, Venudhar Rao Hajari, Narendra Narukulla, Nitin Prasad, Jigar Shah, & Akshay Agarwal. (2024). AI Algorithms for Personalization: Recommender Systems, Predictive Analytics, and Beyond. Darpan International Research Analysis, 12(2), 51–63. Retrieved from https://dira.shodhsagar.com/index.php/j/article/view/41

Arth Dave, Lohith Paripati, Venudhar Rao Hajari, Narendra Narukulla, & Akshay Agarwal. (2024). Future Trends: The Impact of AI and ML on Regulatory Compliance Training Programs. Universal Research Reports, 11(2), 93–101. Retrieved from https://urr.shodhsagar.com/index.php/j/article/view/1257

Arth Dave, Lohith Paripati, Narendra Narukulla, Venudhar Rao Hajari, & Akshay Agarwal. (2024). Cloud-Based Regulatory Intelligence Dashboards: Empowering Decision-Makers with Actionable Insights. Innovative Research Thoughts, 10(2), 43–50. Retrieved from https://irt.shodhsagar.com/index.php/j/article/view/1272

Paripati, L., Prasad, N., Shah, J., Narukulla, N., & Hajari, V. R. (2021). Blockchain-enabled data analytics for ensuring data integrity and trust in AI systems. International Journal of Computer Science and Engineering (IJCSE), 10(2), 27–38. ISSN (P): 2278–9960; ISSN (E): 2278–9979

Narukulla, N., Lopes, J., Hajari, V. R., Prasad, N., & Swamy, H. (2021). Real-time data processing and predictive analytics using cloud-based machine learning. Tuijin Jishu/Journal of Propulsion Technology, 42(4), 91-102 DOI: https://doi.org/10.52783/tjjpt.v42.i4.6757

https://scholar.google.com/scholar?oi=bibs&cluster=13344037983257193364&btnI=1&hl=en

Dave, A., Etikani, P., Bhaskar, V. V. S. R., & Shiva, K. (2020). Biometric authentication for secure mobile payments. Journal of Mobile Technology and Security, 41(3), 245-259. https://scholar.google.com/scholar?cluster=14288387810978696146&hl=en&oi=scholarr

Joel lopes, Arth Dave, Hemanth Swamy, Varun Nakra, & Akshay Agarwal. (2023). Machine Learning Techniques And Predictive Modeling For Retail Inventory Management Systems. Educational Administration: Theory and Practice, 29(4), 698–706. https://doi.org/10.53555/kuey.v29i4.5645

https://kuey.net/index.php/kuey/article/view/5645

Shiva, K., Etikani, P., Bhaskar, V. V. S. R., Palavesh, S., & Dave, A. (2022). The Rise Of Robo-Advisors: Ai-Powered Investment Management For Everyone. Journal of Namibian Studies, 31, 201-214. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Xxl9XwQAAAAJ&citation_for_view=Xxl9XwQAAAAJ:3fE2CSJIrl8C

Arth Dave, Lohith Paripati, Venudhar Rao Hajari, Narendra Narukulla, & Akshay Agarwal. (2024). Future Trends: The Impact of AI and ML on Regulatory Compliance Training Programs. Universal Research Reports, 11(2), 93–101. Retrieved from https://urr.shodhsagar.com/index.php/j/article/view/1257

Shiva, K., Etikani, P., Bhaskar, V. V. S. R., Mittal, A., Dave, A., Thakkar, D., ... & Munirathnam, R. (2024). Anomaly Detection in Sensor Data with Machine Learning: Predictive Maintenance for Industrial Systems. Journal of Electrical Systems, 20(10s), 454-461.https://search.proquest.com/openview/04c95e36f469668009c15b4bd6be4bfd/1?pq-origsite=gscholar&cbl=4433095

Kanchetti, D., Munirathnam, R., & Thakkar, D. (2024). Integration of Machine Learning Algorithms with Cloud Computing for Real-Time Data Analysis. Journal for Research in Applied Sciences and Biotechnology, 3(2), 301–306. https://doi.org/10.55544/jrasb.3.2.46 DOI: https://doi.org/10.55544/jrasb.3.2.46

Thakkar, D., & Kumar, R. (2024). AI-Driven Predictive Maintenance for Industrial Assets using Edge Computing and Machine Learning. Journal for Research in Applied Sciences and Biotechnology, 3(1), 363–367. https://doi.org/10.55544/jrasb.3.1.55 DOI: https://doi.org/10.55544/jrasb.3.1.55

Thakkar, D. (2021). Leveraging AI to transform talent acquisition. International Journal of Artificial Intelligence and Machine Learning, 3(3), 7. https://www.ijaiml.com/volume-3-issue-3-paper-1/

Thakkar, D. (2020, December). Reimagining curriculum delivery for personalized learning experiences. International Journal of Education, 2(2), 7. Retrieved from https://iaeme.com/Home/article_id/IJE_02_02_003

Kanchetti, D., Munirathnam, R., & Thakkar, D. (2019). Innovations in workers compensation: XML shredding for external data integration. Journal of Contemporary Scientific Research, 3(8). ISSN (Online) 2209-0142.

Thakkar, D., Kanchetti, D., & Munirathnam, R. (2022). The transformative power of personalized customer onboarding: Driving customer success through data-driven strategies. Journal for Research on Business and Social Science, 5(2)

Downloads

Published

31-10-2024

Issue

Section

Research Articles

How to Cite

Comparative Analysis of Container Orchestration Platforms: Kubernetes vs. Docker Swarm . (2024). International Journal of Scientific Research in Science and Technology, 11(5), 526-543. https://doi.org/10.32628/IJSRST24105254

Similar Articles

1-10 of 202

You may also start an advanced similarity search for this article.