用复分析方法实现云资源自动调度，大幅减少虚拟机频繁切换。
Intelligent Autonomous Orchestration for Distributed Cloud Resources using Complex-Stability Analysis
- 基于复平面稳定性分析，将监控噪声转化为安全阈值。
- 实时计算稳定性指数，使虚拟机切换减少94%，资源利用率超96%。
- 适合构建抗抖动的智能云调度系统，尤其适用于高并发场景。
在现代分布式云环境中，传统扩容机制常因网络延迟导致云资源抖动。本文提出C-SAS（Complex-Stability Aware Scaling）智能自治编排框架，利用复分析方法实现全局系统均衡。与启发式模型不同，C-SAS作为稳定性感知代理，通过幅角原理和Rouché定理将遥测噪声转化为$s$-平面上的确定性“安全包络”。该算法通过实时计算解析稳定性指数（ASI），智能抑制会降低性能的振荡式扩容操作。实验表明，C-SAS将虚拟机频繁切换减少94%，资源利用率达96%，显著优于标准PID及基于机器学习的自治代理。结果表明，未来具备形式化稳定性约束的AI驱动编排器将是弹性云基础设施的关键。
原文摘要 · Abstract (English)
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.
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