Predictive Autoscaling Using ML Resource Utilization Forecasting
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Solution Overview
Problem
Conventional autoscaling methods, such as those using Horizontal Pod Autoscalers (HPAs), struggle to effectively handle sudden spikes in service load due to their reactive nature, leading to delays, resource waste, and degraded performance.
Innovation Solution
Implementing a machine learning (ML) model for resource utilization forecasting to enable predictive autoscaling by training on historical ratio metrics, allowing proactive scaling based on predicted future resource demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional reactive autoscaling methods are used, then the system can maintain current resource levels, but it cannot anticipate sudden spikes in service load, leading to delays and performance degradation
Solution Approach 1:
The system applies preliminary action by using machine learning models to predict future resource utilization before actual demand spikes occur. The ML model analyzes historical metrics and generates predictions that trigger proactive scaling actions, allowing the system to prepare resources in advance rather than reacting after demand increases. This is evident in the patent where the autoscaler uses predicted metrics to adjust resource allocation before the predicted time window elapses.
Solution Approach 2:
The system implements feedback by continuously monitoring actual resource utilization metrics and comparing them with predicted values. The patent describes a feedback loop where the ML model's predictions are validated against actual metrics, and the system adjusts its scaling decisions based on the accuracy of predictions and current cluster state. This feedback mechanism enables the system to learn and improve its predictive capabilities over time.
2Productivity
If reactive autoscaling is implemented, then the system structure remains simple, but it experiences resource waste and performance degradation during load spikes
Solution Approach 1:
The system performs preliminary scaling actions based on ML predictions to ensure resources are available before demand spikes. By predicting future resource utilization and proactively allocating resources, the system avoids the resource waste associated with reactive scaling that occurs after demand has already increased. The patent shows that the autoscaler adjusts resource allocation based on predicted metrics, preventing both over-provisioning and under-provisioning.
3Reliability
If the system proactively scales resources based on predictions, then service performance is maintained, but the system complexity increases due to ML model integration
Solution Approach 1:
The system uses an intermediary machine learning model that acts as a mediator between historical metrics and scaling decisions. The ML model processes complex patterns in historical data and translates them into actionable predictions, while the patent describes how the autoscaler integrates this model with the existing Kubernetes ecosystem using standard APIs and controllers. This intermediary approach maintains service performance while managing system complexity through modular integration.
4Measurement precision
If historical metrics are processed without smoothing, then the data remains raw and unfiltered, but the ML model accuracy decreases due to noise and anomalies
Solution Approach 1:
The system applies smoothing filters as a preliminary data processing step before feeding metrics to the ML model. The patent describes applying exponential moving average or similar smoothing techniques to historical resource utilization metrics to remove noise and anomalies. This preliminary action improves measurement precision by ensuring the ML model receives clean, processed data, while the complexity is managed through well-established filtering algorithms.
Data Source
AI summary
Certain aspects of the disclosure provide techniques for predictive autoscaling. A method includes determining resource utilization metrics for a plurality of instances of a service running in a container-based cluster for a plurality of timestamps over a period of time; applying a smoothing filter to the resource utilization metrics to obtain smoothed resource utilization metrics; adjusting each of the smoothed resource utilization metrics by a nominal value; calculating a plurality of ratio metrics for the smoothed resource utilization metrics; processing, with a machine learning (ML) model trained to perform resource utilization forecasting, the plurality of ratio metrics and to predict a future ratio metric for the service after a prediction time window; determining a future resource utilization for the service after the prediction time window based on the future ratio metric; and automatically adjusting configuration parameter(s) to modify a state of the container-based cluster based on the future resource utilization.