Intelligent Autonomous Orchestration for Distributed Cloud Resources using Complex-Stability Analysis

TL;DR

This paper introduces C-SAS, an AI-driven autonomous orchestration framework for distributed cloud resources that uses complex stability analysis to improve efficiency and reduce oscillations.

Contribution

It presents a novel stability-aware scaling algorithm based on complex analysis, outperforming traditional heuristic and machine learning methods.

Findings

Reduces VM flapping by 94%

Achieves 96% resource efficiency

Outperforms PID and ML-based agents

Abstract

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 Rouch\'e'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.