Articles Open Access
Intelligent Kubernetes Resource Scheduling and Optimization in Distributed Multi-Cloud Computing Systems
Independent Researcher, USA
Abstract
Modern distributed computing environments increasingly rely on container orchestration platforms such as Kubernetes to manage scalable applications across multi-cloud infrastructures. However, traditional Kubernetes schedulers are primarily rule-based and lack intelligence in optimizing resource allocation across heterogeneous cloud environments. This leads to inefficiencies such as resource underutilization, increased latency, and higher operational costs. Intelligent scheduling using Artificial Intelligence (AI) and Machine Learning (ML) has emerged as a promising solution to enhance decision-making in Kubernetes clusters. AI-driven schedulers analyze workload patterns, resource availability, and system performance metrics to dynamically allocate containers in real time. Techniques such as reinforcement learning, predictive analytics, and optimization algorithms enable adaptive scheduling strategies that improve performance and reduce energy consumption. In multi-cloud environments, intelligent scheduling also considers network latency, data locality, and cost variations across cloud providers. Despite advancements, challenges remain in scalability, interoperability, security, and real-time decision-making. This paper explores AI-based Kubernetes scheduling techniques, optimization strategies, and architectural frameworks for efficient resource management in distributed multi-cloud systems.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 6 |
| Number | 6 |
| Pages | 11403-11410 |
| Published | 2022-11-09 |
| Issue | Vol. 6 No. 6 (2022): International Research Journal of Innovative Engineering (IRJIE) |
References
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2. Bittencourt, L. F., Madeira, E. R., & Da Fonseca, N. L. S. (2015). Virtual machine scheduling in cloud computing: A literature review. IEEE Communications Surveys & Tutorials, 17(4), 2523–2553.
3. Casalicchio, E. (2019). Container orchestration: A survey. Systems Modeling and Simulation Journal, 9(2), 1–20.
4. Ferry, N., Rubén, R., & Dastjerdi, A. V. (2020). Kubernetes-based cloud orchestration: A systematic review. Future Generation Computer Systems, 108, 1–12.
5. Ferrer, A. J., Hernández, F., & Tordsson, J. (2019). Kubernetes scheduling policies and resource management. Journal of Cloud Computing, 8(1), 1–15.
6. Kaur, R., & Chana, I. (2018). A resource scheduling framework for cloud computing environments. Journal of Supercomputing, 74(10), 1–25.
7. Morabito, R. (2017). Virtualization on internet of things edge devices with container technologies. IEEE Cloud Computing, 4(6), 42–49.
8. Nguyen, T. D., Lee, J., & Kim, S. (2020). Reinforcement learning for cloud resource allocation. IEEE Access, 8, 1–14.
9. Rodero-Merino, L., Vaquero, L. M., & Caceres, J. (2011). On the use of virtual machines in cloud environments. IEEE Transactions on Cloud Computing, 2(3), 1–10.
10. Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18.
11. Mathew, A., & Nair, A., & Decouth, J. R. (2019). Audience Behaviour Mining Using Data Analysis. International Journal of Data Structures, 5(2), 1-7.
12. Sudarsan, V., & Sugumar, R. (2018). Building a Distributed K-Means Model using Simple K-Means of Weka.
13. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
14. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
15. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
16. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
17. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
18. Mathew, A. (2019). Cybersecurity infrastructure and security automation. Adv Comput: Int J (ACIJ), 10(6).
19. Sugumar, R. (2018). Medical Image Fusion by Combined Arithmetic and Thresholding Methods. EDITORS OF SPECIAL ISSUE JOURNAL, 17.
20. Balasubramanian, V., & Rajendran, S. (2019). Rough set theory-based feature selection and FGA-NN classifier for medical data classification. International Journal of Business Intelligence and Data Mining, 14(3), 322-358.
21. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
22. Mathew, A. R. Malware Analysis of API Calls using FPGA Hardware Level Security.
23. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
24. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
25. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
26. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
27. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
28. Mathew, A. (2020). Threat intelligence and internet of medical things (IoMT). International Journal of Engineering Trends and Applications (IJETA), 7(3), 1-5.
29. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
30. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
31. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
32. Brijet, Z., & Bharathi, N. (2021). Design of type-2 fuzzy logic controller for fluid catalytic cracking unit. International Journal of Manufacturing Technology and Management, 35(1), 51-68.
33. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
34. Mathew, A. R. Airport Cyber Security and Cyber Resilience Controls. arXiv 2019. arXiv preprint arXiv:1908.09894.
35. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
36. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
37. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
38. Suresh, T., Brijet, Z., & Sheeba, T. B. (2021). CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis. Biomedical Signal Processing and Control, 70, 103034
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