A Framework for Multi-Cloud Network Orchestration Using Ansible and Terraform in Hybrid Environments

With everything going digital, companies rely nowadays on more hybrid and multi-cloud environments, which give them more strength, flexibility, and room to grow. Given the fact that each cloud platform has its own settings, APIs, and services, it is challenging to keep track of network infrastructure across cloud platforms. This paper proposes the use of Terraform for infrastructure provisioning and Ansible for configuration management in order to obtain enhanced network management in multi-cloud hybrid environments. The framework ensures consistency in terminology to refer to network resources across all platforms, such as AWS, Azure, and on-premise data centres, while also enabling automated deployment. The proposed methodology uses Ansible along with Terraform to create testable, changeable, and reproducible deployments. This ensures smoother operations and eliminates the possibility of vendor lock-in. For hybrid infrastructures, automatic configuration of VPN, firewall, and routing configurations is possible with the framework. A real-world implementation example is presented to validate the approach.

Multi-Cloud Hybrid Cloud Network Orchestration Infrastructure as Code (IaC) Ansible Terraform Cloud Automation DevOps Cloud Provisioning

[1] Pabbineedi, S., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., Tyagadurgam, M. S. V., & Gangineni, V. N. (2023). Scalable Deep Learning Algorithms with Big Data for Predictive Maintenance in Industrial IoT. International Journal of AI, BigData, Computational and Management Studies, 4(1), 88-97.

[2] Chalasani, R., Vangala, S. R., Polam, R. M., Kamarthapu, B., Penmetsa, M., & Bhumireddy, J. R. (2023). Detecting Network Intrusions Using Big Data-Driven Artificial Intelligence Techniques in Cybersecurity. International Journal of AI, BigData, Computational and Management Studies, 4(3), 50-60.

[3] Vangala, S. R., Polam, R. M., Kamarthapu, B., Penmetsa, M., Bhumireddy, J. R., & Chalasani, R. (2023). A Review of Machine Learning Techniques for Financial Stress Testing: Emerging Trends, Tools, and Challenges. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(1), 40-50.

[4] Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., Tyagadurgam, M. S. V., Gangineni, V. N., & Pabbineedi, S. (2023). A Survey on Regulatory Compliance and AI-Based Risk Management in Financial Services. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 46-53.

[5] Bhumireddy, J. R., Chalasani, R., Vangala, S. R., Kamarthapu, B., Polam, R. M., & Penmetsa, M. (2023). Predictive Machine Learning Models for Financial Fraud Detection Leveraging Big Data Analysis. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 34-43.

[6] Gangineni, V. N., Pabbineedi, S., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Tyagadurgam, M. S. V. (2023). AI-Enabled Big Data Analytics for Climate Change Prediction and Environmental Monitoring. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 71-79.

[7] Polam, R. M. (2023). Predictive Machine Learning Strategies and Clinical Diagnosis for Prognosis in Healthcare: Insights from MIMIC-III Dataset. Available at SSRN 5495028.

[8] Narra, B., Gupta, A., Polu, A. R., Vattikonda, N., Buddula, D. V. K. R., & Patchipulusu, H. (2023). Predictive Analytics in E-Commerce: Effective Business Analysis through Machine Learning. Available at SSRN 5315532.

[9] Narra, B., Buddula, D. V. K. R., Patchipulusu, H. H. S., Polu, A. R., Vattikonda, N., & Gupta, A. K. (2023). Advanced Edge Computing Frameworks for Optimizing Data Processing and Latency in IoT Networks. JOETSR-Journal of Emerging Trends in Scientific Research, 1(1).

[10] Patchipulusu, H. H. S., Vattikonda, N., Gupta, A. K., Polu, A. R., Narra, B., & Buddula, D. V. K. R. (2023). Opportunities and Limitations of Using Artificial Intelligence to Personalize E-Learning Platforms. International Journal of AI, BigData, Computational and Management Studies, 4(1), 128-136.

[11] Madhura, R., Krishnappa, K. H., Shashidhar, R., Shwetha, G., Yashaswini, K. P., & Sandya, G. R. (2023). UVM Methodology for ARINC 429 Transceiver in Loop Back Mode. IEEE.

[12] Shashidhar, R., Kadakol, P., Sreeniketh, D., Patil, P., Krishnappa, K. H., & Madhura, R. (2023). EEG Data Analysis for Stress Detection Using K-Nearest Neighbor. IEEE.

[13] Krishnappa, K. H., & Trivedi, S. K. (2023). Efficient and Accurate Estimation of Pharmacokinetic Maps from DCE-MRI Using Extended Tofts Model in Frequency Domain.

[14] Krishnappa, K. H., Shashidhar, R., Shashank, M. P., & Roopa, M. (2023). Detecting Parkinson's Disease with Prediction: A Novel SVM Approach. IEEE.

[15] Shashidhar, R., Balivada, D., Shalini, D. N., Krishnappa, K. H., & Roopa, M. (2023). Music Emotion Recognition Using Convolutional Neural Networks for Regional Languages. IEEE.

[16] Madhura, R., Krishnappa, K. H., Manasa, R., & Yashaswini, K. P. (2023). Slack Time Analysis for APB Timer Using Genus Synthesis Tool. Springer.

[17] Krishnappa, K. H., & Gowda, N. V. N. (2023). Dictionary-Based PLS Approach to Pharmacokinetic Mapping in DCE-MRI Using Tofts Model. Springer.

[18] Krishnappa, K. H., & Gowda, N. V. N. (2023). Dictionary-Based PLS Approach to Pharmacokinetic Mapping in DCE-MRI Using Tofts Model. Springer.

[19] Madhura, R., Krutthika Hirebasur Krishnappa. et al. (2023). Slack Time Analysis for APB Timer Using Genus’s Synthesis Tool. ICT4SD.

[20] Shashidhar, R., Aditya, V., Srihari, S., Subhash, M. H., & Krishnappa, K. H. (2023). Empowering Investors: Insights from Sentiment Analysis, FFT, and Regression in Indian Stock Markets. IEEE.

[21] Bhumireddy, J. R., Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., & Penmetsa, M. (2023). Predictive Models for Early Detection of Chronic Diseases in Elderly Populations: A Machine Learning Perspective. Int J Comput Artif Intell, 4(1), 71-79.