How do you design clustering systems with dynamic resource scaling under variable load?

Dynamic scaling adjusts compute resources based on workload, using autoscaling clusters and workload prediction.

Clustering workloads are highly variable depending on batch size or streaming load. Systems use autoscaling policies to add or remove compute nodes dynamically. Predictive scaling uses historical workload patterns to pre-allocate resources. This prevents latency spikes and ensures cost efficiency. Kubernetes or Spark clusters typically handle orchestration.