Predictive Autoscaling in Kubernetes Microservices with KEDA and Time Series Forecasting

Abstract

Cloud-native microservices architectures handling large volumes of transactions require smart resource management mechanisms that go beyond traditional reactive autoscaling boundaries. Threshold-based scaling is traditional, bringing with it the natural latency between demand changes and capacity changes, leading to compromised performance during traffic spikes and wastage of resources during dips in demand. Event-driven autoscaling systems go beyond infrastructure-level metrics by involving external data feeds, message queue sizes, and application-level metrics for making scaling decisions. Yet, reactive mechanisms are inherently bounded by response latencies that degrade service quality and operational effectiveness. Predictive autoscaling bridges such gaps with time series forecasting models that interpret historical workload patterns to predict future resource needs. The combination of deep neural network architectures with event-driven autoscaling modules allows for proactive capacity provisioning in response to projected workload trends as opposed to observed metric values. Multivariate prediction models with correlated resource metrics are superior in prediction accuracy over univariate methods, able to capture intricate interdependencies between CPU usage, memory usage, network bandwidth, and storage activities. Design considerations include model choice based on workload behaviour, hyperparameter tuning to balance accuracy with computational cost, and reliable integration frameworks with extensive error handling and fallback strategies. Predictive approaches show considerable benefits such as lower response time degradation, better cost-effectiveness with enhanced resource utilisation, and reduced scaling operation frequency. The challenges are to ensure accuracy of the forecast in the face of changing traffic patterns, address computational overhead due to periodic model retraining, and provide tolerance to prediction uncertainties affecting the scaling aggressiveness.