A Framework for Distributed Data Intelligence in Event Driven Microservices Based High Throughput Computing Environments

Giovanni Tanaka

Authors

Giovanni Tanaka Research Analyst, United Kingdom Author

Keywords:

Distributed Data Intelligence, Event-Driven Microservices, High-Throughput Computing, Stream Processing, Adaptive Frameworks

Abstract

This paper proposes a novel framework for integrating distributed data intelligence into event-driven microservice architectures operating within high-throughput computing (HTC) environments. As modern systems generate massive, velocity-driven data streams, traditional batch processing and centralized intelligence models fail to provide real-time insights and adaptive scaling. The framework leverages event sourcing, stream processing engines (e.g., Apache Kafka, Flink), and lightweight machine learning models deployed as sidecar microservices. Key contributions include a decentralized data governance model, an adaptive event prioritization mechanism, and a feedback loop for continuous model retraining. Evaluations in a simulated HTC environment show a 34% reduction in latency for anomaly detection and a 41% improvement in resource utilization compared to monolithic baselines. This work references foundational concepts from event-driven architectures [1], stream processing [2], and microservices patterns [3], while extending them with distributed intelligence capabilities suitable for exascale data throughput.

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