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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.08139 |
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| _version_ | 1866918490665910272 |
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| author | Shyam, Gopal Krishna Bharti, Priyanka |
| author_facet | Shyam, Gopal Krishna Bharti, Priyanka |
| contents | In modern distributed cloud environments, efficient resource allocation is required as traditional scaling mechanisms are often subject to cloud thrashing due to network-induced latencies. In this paper, we propose C-SAS (Complex-Stability Aware Scaling), an intelligent autonomous orchestration framework that leverages complex analytic methods to achieve system-wide equilibrium. In contrast to heuristic-based models, C-SAS acts as a stability-aware agent, converting telemetry noise into a deterministic "Safety Envelope" on the $s$-plane using the Argument Principle and Rouché's Theorem. The algorithm smartly suppresses oscillatory scaling operations that would otherwise degrade performance, by computing a real-time Analytic Stability Index (ASI). The experimental results show that C-SAS reduces VM flapping by 94\%, and achieves 96\% resource efficiency, significantly outperforming standard PID and ML-based autonomous agents. Our results suggest that future resilient autonomous cloud infrastructures will require AI-driven orchestrators with built-in formal stability constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08139 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Intelligent Autonomous Orchestration for Distributed Cloud Resources using Complex-Stability Analysis Shyam, Gopal Krishna Bharti, Priyanka Distributed, Parallel, and Cluster Computing Artificial Intelligence In modern distributed cloud environments, efficient resource allocation is required as traditional scaling mechanisms are often subject to cloud thrashing due to network-induced latencies. In this paper, we propose C-SAS (Complex-Stability Aware Scaling), an intelligent autonomous orchestration framework that leverages complex analytic methods to achieve system-wide equilibrium. In contrast to heuristic-based models, C-SAS acts as a stability-aware agent, converting telemetry noise into a deterministic "Safety Envelope" on the $s$-plane using the Argument Principle and Rouché's Theorem. The algorithm smartly suppresses oscillatory scaling operations that would otherwise degrade performance, by computing a real-time Analytic Stability Index (ASI). The experimental results show that C-SAS reduces VM flapping by 94\%, and achieves 96\% resource efficiency, significantly outperforming standard PID and ML-based autonomous agents. Our results suggest that future resilient autonomous cloud infrastructures will require AI-driven orchestrators with built-in formal stability constraints. |
| title | Intelligent Autonomous Orchestration for Distributed Cloud Resources using Complex-Stability Analysis |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2605.08139 |