Marin LitoiuFehresti, Sara2025-07-232025-07-232025-05-142025-07-23https://hdl.handle.net/10315/43056Cloud computing, as the backbone of modern adaptive software architectures, has revolutionized data storage and processing, driven by the power and flexibility of microservices. Despite their advantages in fault isolation and flexible deployment, microservices often experience unpredictable latency spikes, leading to costly Service Level Agreement (SLA) violations. This thesis introduces a multiscale time-spectrum framework, called GRASP (Graph-based SLA Breach Prediction). It leverages time-series data, sequence processing, and graph-based modeling to proactively detect performance anomalies and predict SLA breaches in microservice-based systems within upcoming time windows. In our framework, raw data is transformed into graph representations and fed into deep learning models to capture both topological and temporal characteristics. By combining graph analysis with sequential modeling, our dual approach not only identifies critical service dependencies but also pinpoints potential end-to-end bottlenecks. Evaluations on microservice datasets demonstrate its superiority over baseline methods in early warnings, forecasting breaches, and localizing root causes at the service level, underscoring its potential to enhance the reliability and efficiency of microservice-based applications in cloud environments.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Information technologyArtificial intelligenceComputer scienceElectronic Thesis or Dissertation2025-07-23Service Level Agreements Cloud Computing Microservices Latency Prediction Graph Neural Networks SLA Breach Detection Deep Learning Cloud-Native Systems Performance Monitoring