Citation :

Sachin H. Patel, Amit Nayak, "An Integrated Secure and Energy-Efficient Cloud Architecture using Resource Consolidation and Machine Learning-Based Optimization," International Journal of Electronics and Communication Engineering, vol. 13, no. 5, pp. 109-128, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I5P111

Abstract

The rapid adoption of cloud computing has significantly increased data center energy consumption but has also heightened the risk of data integrity in the multi-tenant setup. Current alternatives usually cover the issues of energy efficiency and security separately, which results in a non-optimal compromise of sustainability and integrity maintenance. The paper proposes an Integrated Energy Integrity Cloud Optimization Framework, which will reduce the energy usage and the risk of data distortion simultaneously with a single multi-objective optimization framework. The model integrates integrity-conscious virtual machine aggregation, machine learning workload forecasting, and dynamic auto-scaling in a cloud-native AWS setup. The formal mathematical formulation integrates energy minimization with integrity risk constraints while ensuring SLA compliance. The system is deployed with the help of Amazon EC2, Auto Scaling, Lambda, Cognito, SageMaker, and CloudWatch services, and tested on the publicly available dataset of Google Cluster Workload Trace with a known control of the integrity events simulation. The experimental results show a 20%–25% reduction in energy consumption. Comparative analysis proves that the suggested framework is more efficient than energy-only and security-only frameworks, as it is possible to optimize sustainability and data integrity at the same time with the help of predictive and risk-aware resource management.

Keywords

Energy-Efficient Cloud Computing, Data Integrity, Virtual Machine Consolidation, Machine Learning-Based Scaling, Multi-Objective Optimization, Green Cloud Architecture, AWS Cloud Implementation, Sustainable Data Centers.

References

1. Sahul Goyal, and Lalit Kumar Awasthi, “Adaptive Multi-Objective Virtual Machine Consolidation for Energy-Efficient Cloud Data Centers,” Journal of Grid Computing, vol. 23, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
2. Abdullah Alouran et al., “Energy Efficient Virtual Machines Placement in Cloud Datacenters using Genetic Algorithm and Adaptive Thresholds,” PLoS One, vol. 19, no. 1, pp. 1-19, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
3. Deep Bodra, and Sushil Khairnar, “Machine Learning-Based Cloud Resource Allocation Algorithms: A Comprehensive Comparative Review,” Frontiers in Computer Science, vol. 7, pp. 1-17, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Jin Qiu, Lan Shu, and Yinshun Zhang, “The Deep Learning-Based Security Assessment and Optimization Model for Enterprise Information Systems Under Digital Economy,” Journal of Organizational and End User Computing, vol. 37, no. 1, pp. 1-52, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Aravind Nuthalapati, “Cloud Data Center Performance Optimization through Machine Learning-Based Workload Forecasting and Energy Efficiency,” International Journal of Science and Research Archive, vol. 13, no. 2, pp. 2353-2361, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Abhilasha Chauhan, and Suchi Johari, Machine Learning approaches for Effective Energy-Efficient Resource Management Strategies in Cloud Services, Advanced Computing Techniques for Optimization in Cloud, Chapman and Hall/CRC, pp. 65-86, 2024.
   [Google Scholar] [Publisher Link]
7. TA Gamage, and Indika Perera, “Optimizing Energy Efficient Cloud Architectures for Edge Computing: A Comprehensive Review,” International Journal of Advanced Computer Science & Applications, vol. 15, no. 11, pp. 1-9, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Thandar Thein et al., “Reinforcement Learning Based Methodology for Energy-Efficient Resource Allocation in Cloud Data Centers,” Journal of King Saud University-Computer and Information Sciences, vol. 32, no. 10, pp. 1127-1139, 2020.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Anna Kushchazli et al., “Evaluating QoS in Dynamic Virtual Machine Migration: A Multi-Class Queuing Model for Edge-Cloud Systems,” Journal of Sensor and Actuator Networks, vol. 14, no. 3, pp. 1-23, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Raseena M. Haris et al., “Enhancing Security and Performance in Live VM Migration: A Machine Learning-Driven Framework with Selective Encryption,” Expert Systems, vol. 42, no. 2, pp. 1-15, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
11. Paromita Goswami et al., “Investigation on Storage Level Data Integrity Strategies in Cloud Computing: Classification, Security Obstructions, Challenges and Vulnerability,” Journal of Cloud Computing, vol. 13, pp. 1-23, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
12. Samar Hussni Anbarkhan, “Optimizing Cloud Resource Allocation with Machine Learning: Strategies for Efficient Computing,” Information Systems Engineering, vol. 30, no. 1, pp. 1-9, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Dhruvi Thakkar, Vaibhav C. Gandhi, and Dhriti Trivedi, “Forecasting Maternal Women's Health Risks using Random Forest Classifier,” 2024 International Conference on Inventive Computation Technologies (ICICT), Lalitpur, Nepal, pp. 961-965, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Hassan Raza, “Deep Learning-based Optimization Techniques for Large-scale Data Processing in Cloud Environments,” Multidisciplinary Research in Computing Information Systems, vol. 5, no. 12, pp. 1214-1222, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Neeraj Kumar Pandey et al., “Energy Efficiency Strategy for Big Data in Cloud Environment using Deep Reinforcement Learning,” Mobile Information Systems, vol. 2022, no. 1, pp. 1-11, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
16. Suraj Singh Panwar et al., “Machine Learning Approaches for Efficient Energy Utilization in Cloud Data Centers,” Procedia Computer Science, vol. 235, pp. 1782-1792, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
17. M. Amutha et al., “Efficient Cloud Resource Management using Complex-Value Spatio-Temporal Graph Convolutional Neural Network,” Journal of Circuits, Systems and Computers, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Jing Bi et al., “Energy-Optimized Partial Computation Offloading in Mobile-Edge Computing with Genetic Simulated-Annealing-Based Particle Swarm Optimization,” IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3774-3785, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
19. Devesh Srivastava et al., “Auto-Scaling of Cloud Applications Using Machine Learning,” 2025 International Conference on Next Generation of Green Information and Emerging Technologies (GIET), Gunupur, India, pp. 1-6, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
20. Abdelhadi Amahrouch, Youssef Saadi, and Said El Kafhali, “Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy,” Network, vol. 5, no. 2, pp. 1-24, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
21. Sahul Goyal, and Lalit Kumar Awasthi, “EBWO-GE: An Innovative Approach to Dynamic VM Consolidation for Cloud Data Centers,” Concurrency and Computation: Practice and Experience, vol. 36, no. 28, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
22. N. Moocheet et al., “Minimum-Energy Virtual Machine Placement using Embedded Sensors and Machine Learning,” Future Generation Computer Systems, vol. 161, pp. 85-94, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
23. Francisco Javier Maldonado-Carrascosa et al., “Game Theory-Based Virtual Machine Migration for Energy Sustainability in Cloud Data Centers,” Applied Energy, vol. 372, pp. 1-16, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
24. Zeinab Khodaverdian et al., “An Energy Aware Resource Allocation based on Combination of CNN and GRU for Virtual Machine Selection,” Multimedia Tools and Applications, vol. 83, pp. 25769-25796, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
25. G. Guna et al., “Multi-Objective Genetic Algorithms for Dynamic Resource Optimization in Cloud Computing,” 2025 International Conference on Networks and Cryptology (NETCRYPT), New Delhi, India, pp. 876-881, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
26. Nirmal Kr. Biswas et al., “Design of an Energy Efficient Dynamic Virtual Machine Consolidation Model for Smart Cities in Urban Areas,” Intelligent Data Analysis: An International Journal, vol. 27, no. 5, pp. 1409-1431, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
27. Elham Hormozi et al., “Energy-Efficient Virtual Machine Placement in Data Centres via an Accelerated Genetic Algorithm with Improved Fitness Computation,” Energy, vol. 252, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
28. Yashwant Singh Patel, Rishabh Jaiswal, and Rajiv Misra, “Deep Learning-Based Multivariate Resource Utilization Prediction for Hotspots and Coldspots Mitigation in Green Cloud Data Centers,” The Journal of Supercomputing, vol. 78, pp. 5806-5855, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
29. Kawsar Haghshenas et al., “Magnetic: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers,” IEEE Transactions on Services Computing, vol. 15, no. 1, pp. 30-44, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
30. Soha Rawas, Ahmed Zekri, and Ali El-Zaart, “LECC: Location, Energy, Carbon and Cost-Aware VM Placement Model in Geo-Distributed DCs,” Sustainable Computing: Informatics and Systems, vol. 33, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
31. S. Parthasarathy, “Secure Virtual Machine Migration and Host Overload Detection using Modified Pelican Optimization with Variable Load Mean Function,” Journal of Circuits, Systems and Computers, vol. 33, no. 14, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
32. Jie Yuan et al., “Elevating Security in Migration: An Enhanced Trusted Execution Environment-based Generic Virtual Remote Attestation Scheme,” Information, vol. 15, no. 8, pp. 1-17, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]

33. Rukshanda Kamran, Ali A. El-Moursy, and Amany Abdelsamea, “Efficient HPC and Energy-Aware Proactive Dynamic VM Consolidation in Cloud Computing,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 10, pp. 1-12, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]

34. Yousef Sanjalawe et al., “AI-Driven Job Scheduling in Cloud Computing: A Comprehensive Review,” Artificial Intelligence Review, vol. 58, pp. 1-113, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
35. Mohammed E. Seno, Ban N. Dhannoon, and Omer K. Jasim Mohammad, “Enhancement of Cloud Computing Environment using Machine Learning Algorithms MLCE,” Iraqi Journal of Computers, Communications, Control & Systems Engineering, vol. 23, no. 4, pp. 1-12, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
36. Hassan Mahmood Khan, Fang-Fang Chua, and Timothy Tzen Vun Yap, “A Review on Quality-of-Service Monitoring, Violation and Remediation for the Cloud,” Journal of System and Management Sciences, vol. 13, no. 5, pp. 107-126, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
37. Yizhe Chen, Enmiao Feng, and Zhipeng Ling, “Energy-Efficient and Secure Resource Allocation in Cloud Computing using Deep Reinforcement Learning,” Journal of Advanced Computing Systems, vol. 4, no. 11, pp. 1-15, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
38. S. Mahipal, and V. Ceronmani Sharmila, “A Security Framework Protecting Virtual Machines against Attacks on Migration and Persistence in Cloud Computing Environment,” Journal of Theoretical and Applied Information Technology, vol. 101, no. 11, pp. 1-14, 2023.
    [Google Scholar] [Publisher Link]
39. S. Parthasarathy, “OSVR: An Efficient Support Vector Regression Model-Based Host Overload Detection and Secure Virtual Machine Migration,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 7309-7317, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
40. Garima Verma et al., “Secure VM Migration in Cloud: Multi-Criteria Perspective with Improved Optimization Model,” Wireless Personal Communications, vol. 124, pp. 75-102, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]