autoscalingot

Autoscalingot is a framework and set of practices for automatic resource scaling in cloud-native environments. It extends traditional autoscaling by integrating predictive workload forecasting, cross-service coordination, and policy-driven decisions to optimize both performance and cost.

The architecture typically comprises telemetry collectors, a predictive model engine, a policy engine, a coordination layer, and actuators. Telemetry gathers metrics such as request rate, latency, CPU, memory, queue depth, and error rates. The forecasting model projects near-term demand, while the policy engine translates forecasts into scaling actions, and the actuators adjust replicas, containers, or serverless concurrency.

Key features include proactive scaling, cross-service coordination to avoid conflicting actions, cost-aware policies, adherence to service-level objectives, anomaly detection, safety bounds, cooldown windows, and rollback capabilities. These elements work together to anticipate load and adjust capacity before performance degrades, while maintaining control over resource usage.

Applications include web applications, microservices, data pipelines, and event-driven systems. Benefits include improved tail latency, reduced over-provisioning, and better resource utilization. Limitations involve implementation complexity, reliance on accurate forecasts, risk of thrashing under highly volatile workloads, and additional monitoring requirements.

Autoscalingot is discussed primarily in theoretical and experimental contexts and is not a widely standardized term. In practice, organizations may implement autoscalingot-like behavior by integrating predictive autoscalers with existing Kubernetes Horizontal Pod Autoscalers or cloud provider scaling features.