ATLAS: Learning to Rewire the Web — Predictive Architecture Transformation for Autonomous Cloud Systems

Modern e-commerce platforms must handle sudden and unpredictable traffic surges caused by flash sales, festive shopping events, and viral online activity. Traditional web architectures typically adopt one of two extremes: a tightly coupled monolithic design that provides low latency but becomes fragile under heavy load, or a loosely coupled microservices architecture that improves scalability and resilience but introduces communication overhead during normal operation. This trade-off forces system designers to choose between performance efficiency and scalability robustness. This paper introduces ATLAS (Adaptive Traffic-aware Loose–tight Architecture System), a next-generation adaptive web architecture that dynamically adjusts its coupling strategy based on real-time system conditions. ATLAS employs machine learning models to analyse operational telemetry, predict traffic surges, detect anomalies, and forecast potential system failures. Using these predictions, the architecture can automatically transform its runtime structure, switching between tightly coupled monolithic execution and loosely coupled microservices deployment as traffic conditions evolve. To improve reliability, ATLAS incorporates a self-healing recovery pipeline that autonomously detects service failures, isolates faulty components, and restores normal operation without human intervention. Through case studies of large-scale platforms such as Google Search, Amazon, and Flipkart, we illustrate how existing systems can evolve toward the ATLAS paradigm, enabling self-adaptive and resilient web infrastructures for the next generation of large-scale online services.