SmartScale: Real-Time AI-Driven Resource Optimization for Cloud Microservices
DOI:
https://doi.org/10.15662/IJEETR.2024.0604016Keywords:
Cloud resource optimization, Microservices, Demand forecasting, Autoscaling, Kubernetes right-sizing, FinOps, E-commerce, Service level objectivesAbstract
E-commerce platforms experience highly variable demand driven by promotions, flash sales, and seasonal peaks. Static provisioning wastes capacity during normal periods, while reactive autoscaling responds too slowly to sudden surges, creating a persistent trade-off between cloud cost and application reliability.
This study presents SmartScale, a closed-loop optimization framework for containerized microservices running on Kubernetes in AWS. SmartScale combines live infrastructure telemetry and business signals with short-horizon and seasonal demand forecasting to drive three actions: proactive replica scaling, resource request right-sizing, and reclamation of idle capacity. All actions pass through safety guardrails that enforce service level objectives (SLOs), disruption budgets, and canary validation with automatic rollback. The framework was evaluated on an e-commerce microservice platform against static provisioning and threshold-based reactive autoscaling.
Compared with the reactive baseline, SmartScale reduced monthly compute cost by 28-38%, decreased scaling response time from several minutes to under 60 seconds in anticipation of forecasted surges, and reduced SLO violation minutes by approximately 60% during peak events. Average cluster CPU utilization increased from 22-30% to 48-60%. No customer-facing incidents were attributable to automated reclamation actions.
Conclusions: Integrating demand forecasting with guarded, closed-loop actuation allows cloud-hosted e-commerce systems to reduce cost without sacrificing reliability. The framework provides a replicable pattern for organizations seeking to move from reactive capacity management toward predictive, policy-bound optimization.
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