Serverless Computing Optimization Strategies Using ML-Based Auto-Scaling and Event-Stream Intelligence for Low-Latency Enterprise Workloads
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I3P113Keywords:
Serverless Computing, Function-As-A-Service (Faas), Predictive Auto-Scaling, Event-Stream Intelligence, Event-Driven ArchitecturesAbstract
This paper presents a framework of optimization of serverless computing as a combination of machine learning-driven predictive auto-scaling and event-stream intelligence, to serve low-latency enterprise workloads. Conventional function-as-a-service (FaaS) systems tend to exhibit cold starts, bursty usage, and obscure resource assignment and all these come in the form of tail-latency violations to business-critical workloads like real-time analytics, digital payments, and IoT backends. To overcome these difficulties, the framework uses the past trace of workload patterns and the current telemetry to predict demand and preemptive warm instances and the best configuration parameters including concurrency limits and memory footprints. The architecture incorporates an event-stream intelligence layer, which is constantly scanning message queues, logs and domain events in order to spot anomalies, micro-bursts, and seasonality in traffic. These insights are combined with the predictions of the ML to adjust dynamically the scaling policies of serverless functions and supporting services with the cost and SLO constraints to a closed-loop controller. Synthetic benchmarks and enterprise-inspired case studies show that the experimental evaluation of p95 and p99 latency, a lower cold-start frequency and a better cost performance ratio are much better than reactive and threshold-based scaling baselines. These findings, to practitioners, underscore the effectiveness of utilizing predictive modeling alongside event-based observability to turn serverless platforms into robust substrates to support mission-critical, latency-sensitive applications in any modern enterprise as well as provide them with a blueprint to be systematically reused
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