AI- and Machine Learning-Enabled Secure Scalable Cloud Architectures for Network Reliability Privacy Preservation and Energy Optimization
DOI:
https://doi.org/10.15662/IJARCST.2021.0405007Keywords:
AI-enabled cloud architecture, machine learning, network reliability, privacy preservation, cybersecurity, energy optimization, scalable systems, predictive analytics, intelligent resource management, cloud computingAbstract
The rapid adoption of cloud computing has intensified the need for secure, reliable, and energy-efficient architectures capable of supporting large-scale intelligent applications. AI- and machine learning-enabled cloud architectures provide advanced capabilities for adaptive network management, proactive threat detection, and optimized resource utilization. This paper presents a secure and scalable cloud architecture that integrates machine learning models for network reliability enhancement, privacy-preserving mechanisms for sensitive data protection, and intelligent energy optimization techniques. The proposed framework employs predictive analytics to anticipate network failures, anomaly detection models to mitigate cyber threats, and privacy-aware learning approaches to ensure data confidentiality across distributed environments. In addition, energy-efficient scheduling and workload optimization strategies are incorporated to reduce operational costs and environmental impact. The architecture is designed to support heterogeneous workloads and dynamic traffic conditions while maintaining high availability and compliance with security standards. Experimental analysis and architectural evaluation demonstrate improved network resilience, enhanced privacy preservation, and significant gains in energy efficiency, making the proposed solution suitable for next-generation cloud and edge computing ecosystems.References
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