C-SAS Framework Enhances Distributed Cloud Stability via Complex Analysis
Researchers Gopal Krishna Shyam and Priyanka Bharti have proposed C-SAS (Complex-Stability Aware Scaling), a novel intelligent autonomous orchestration framework designed to optimize resource allocation in distributed cloud environments. Traditional scaling mechanisms often suffer from cloud thrashing caused by network-induced latencies, leading to performance degradation. C-SAS addresses this by leveraging complex analytic methods, specifically the Argument Principle and Rouché's Theorem, to convert telemetry noise into a deterministic Safety Envelope on the s-plane. This approach allows the system to compute a real-time Analytic Stability Index (ASI), effectively suppressing oscillatory scaling operations that cause VM flapping. Experimental results demonstrate that C-SAS reduces VM flapping by 94% and achieves 96% resource efficiency, significantly outperforming standard PID controllers and existing machine learning-based autonomous agents. The study suggests that future resilient cloud infrastructures will require AI-driven orchestrators integrated with formal stability constraints to ensure system-wide equilibrium. This academic submission highlights a significant advancement in applying mathematical stability theory to modern cloud computing challenges.
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C-SAS Framework Enhances Distributed Cloud Stability via Complex Analysis
Researchers Gopal Krishna Shyam and Priyanka Bharti have proposed C-SAS (Complex-Stability Aware Scaling), a novel intelligent autonomous orchestration framework designed to optimize resource allocation in distributed cloud environments. Traditional scaling mechanisms often suffer from cloud thrashing caused by network-induced latencies, leading to performance degradation. C-SAS addresses this by leveraging complex analytic methods, specifically the Argument Principle and Rouché's Theorem, to convert telemetry noise into a deterministic Safety Envelope on the s-plane. This approach allows the system to compute a real-time Analytic Stability Index (ASI), effectively suppressing oscillatory scaling operations that cause VM flapping. Experimental results demonstrate that C-SAS reduces VM flapping by 94% and achieves 96% resource efficiency, significantly outperforming standard PID controllers and existing machine learning-based autonomous agents. The study suggests that future resilient cloud infrastructures will require AI-driven orchestrators integrated with formal stability constraints to ensure system-wide equilibrium. This academic submission highlights a significant advancement in applying mathematical stability theory to modern cloud computing challenges.
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