Some common auto-scaling algorithms used in architecturally resilient systems are:
1. Rule-Based Scaling: In this algorithm, a set of rules is defined based on certain parameters such as CPU usage, memory consumption, network traffic, etc. When these parameters cross a threshold value, the infrastructure automatically adds or removes resources to maintain the desired level of performance.
2. Predictive Scaling: This algorithm uses historical data to predict future resource needs. Machine learning algorithms are used to analyze the patterns and trends in the data, and based on these predictions, the infrastructure automatically scales up or down.
3. Event-Based Scaling: In this algorithm, resource scaling is triggered by specific events such as sudden spikes in traffic, a hardware failure, or a software update. The infrastructure automatically scales up or down to handle the event.
4. Hybrid Scaling: This algorithm combines rule-based and predictive scaling to achieve optimal performance. It uses rule-based scaling to handle normal usage patterns and predictive scaling to handle unexpected spikes in traffic or other unusual events.
5. Proportional Scaling: In this algorithm, resources are added or removed in proportion to the workload. This ensures that the infrastructure scales in a predictable and consistent manner, avoiding sudden spikes or drops in performance.
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