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Event-driven microservices auto-scaling system using Kafka and multi-model ML consensus. Services emit metrics, classifiers vote on scaling actions, an authoritative controller aggregates decisions, and Kubernetes executes them. Includes persistence, retraining loop, and fault-tolerant control logic.
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7
commits
Implement ML-driven Kafka-based autoscaling system with ensemble voting
f02caa7View on GitHubAdd constant latency investigation, SLA threshold update, and 4-hour mixed pattern test
f781d7fView on GitHubfeat(load-testing): Add EuroSys'24 stochastic load testing implementation
4a8306fView on GitHub