Benchmarking Predictive Autoscaling vs. Horizontal Scaling for Large-Scale Data Pipelines: A Real-Time FinOps Evaluation - Deepika Annam (International Journal of Computational and Experimental Science and Engineering, 2026)
Autoscaling is an essential facilitator of stability of performance and cost at cloud-native data platforms, but most empirical analyses are conducted with stateless web services and microservice-based architectures as opposed to data engineering workloads. A common set of data pipelines, such as ETL flows, streaming analytics, batch processing, and Spark-based distributed computations, uses the characteristics of stateful execution, bursty demand patterns, DAG-based scheduling, and extreme sensitivity to scaling latency. Machine intelligence-based predictive autoscaling methods like ARIMA, Prophet, LSTM, and workload archetype classifiers have the potential to provide proactive resource provisioning in the future by anticipating a demand value, thus eliminating SLA breaches and tail latency. But these predictive strategies are not done with strict benchmarking relative to the conventional reactive horizontal autoscaling in the context of data-centric workloads running in the domain of real-world variability. At the same time, FinOps structures promote cloud cost visibility and efficiency, but there is no standardized metric that can measure the cost-performance trade-offs of predictive and reactive scaling schemes in a variety of pipeline structures. This benchmark presents a full-system assessment model that integrates workload traces recorded in the past with synthetic replay workloads replicating enterprise data pipeline behavior, including bursty ingestion, periodic batch cycles, and multi-stage DAG executions. The predictive, reactive, and hybrid autoscaling options are tested on Kubernetes and Spark clusters with an extensive metric suite, which combines engineering performance indicators with cost-related dimensions and FinOps-oriented metrics. The resulting construct gives objective foundations of autoscaling decision-making and provides real-world guidelines and artifacts of evaluation to organizations that want to streamline cloud spending without sacrificing pipeline stability or operational robustness.