Authors: Srinivasa Chakravarthy Seethala
Abstract: Real time event streaming pipelines form the operational backbone of modern digital platforms, supporting continuous data ingestion, processing, and delivery across cloud native and distributed environments. Despite their importance, reliability management in such pipelines remains largely reactive, relying on threshold based monitoring and post failure diagnostics that are insufficient for preventing cascading disruptions. This study addresses the problem of anticipating reliability degradation in real time event streaming systems by proposing a predictive reliability engineering framework grounded in multi modal deep learning. The primary objective is to enable early identification of failure precursors by jointly analyzing heterogeneous telemetry sources, including system metrics, execution logs, distributed traces, and event level metadata. A mixed method research approach is adopted, combining quantitative modeling of historical incident data with qualitative architectural analysis of streaming platforms to inform model design and integration. The proposed framework employs temporal and representation learning techniques to fuse multi modal signals and generate probabilistic reliability risk scores ahead of observable failures. Experimental evaluation across representative streaming workloads demonstrates improved failure prediction accuracy, longer warning lead times, and reduced false alert rates compared to single source monitoring baselines. The findings highlight the innovation of multi modal fusion for reliability prediction and its implications for proactive operational decision making. From an academic perspective, the study advances reliability engineering by introducing predictive, data driven models tailored to real time pipelines. From an industry standpoint, the framework supports more resilient event driven architectures through earlier intervention, reduced downtime, and improved service continuity, reinforcing the strategic value of intelligent reliability management in high availability systems.
DOI: http://doi.org/10.5281/zenodo.17938555
