Designing Self Adaptive Cloud and AI Platforms for High Performance Secure and Intelligent Systems
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Abstract
The rapid evolution of cloud computing and artificial intelligence has created the need for platforms that are not only scalable but also capable of adapting dynamically to changing workloads, security threats, and performance requirements. This paper explores the design of self-adaptive cloud and AI platforms that integrate intelligent decision-making mechanisms to optimize system performance, enhance security, and ensure resilience. These platforms leverage machine learning, real-time monitoring, and automated orchestration to continuously analyze system behavior and make autonomous adjustments. Key design principles include elasticity, fault tolerance, predictive analytics, and zero-trust security models. The study highlights how adaptive systems can improve resource utilization, reduce latency, and proactively mitigate cyber threats. Furthermore, it discusses architectural frameworks that combine distributed computing, edge intelligence, and hybrid cloud environments. By incorporating self-learning capabilities, these platforms can evolve over time, responding effectively to emerging challenges and workload variability. The paper concludes by emphasizing the importance of integrating AI-driven automation with cloud infrastructure to build intelligent systems that are efficient, secure, and capable of sustaining high performance in dynamic environments.
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