Insight-Driven Framework for Resolving Performance Constraints in Modular Service Ecosystems
Mohammed Junead Sheriff Zahid · 2025
Modern modular service ecosystems rely on microservices for scalability and flexibility, yet performance constraints such as bottlenecks and resource contention remain critical challenges. This research presents a novel insight-driven framework for detecting and mitigating performance anomalies in microservices-based applications. By leveraging graph-based learning models and multi-source telemetry analysis, our approach effectively identifies independent, dependent, and cascading bottlenecks, facilitating timely intervention. We introduce and evaluate GAMMA, an advanced anomaly detection and localization model that integrates attention-based graph convolution networks with a multi-expert learning mechanism. Empirical evaluations on large-scale datasets demonstrate that GAMMA significantly outperforms existing methods in detecting and diagnosing multiple bottlenecks, achieving an F1 score of 0.92 for anomaly detection and 0.89 for bottleneck localization. The proposed framework enhances performance management in modular service architectures, ensuring resilient and adaptive cloud-native applications.