Optimization Opportunities for Cloud-Based Data Pipeline Infrastructures

A team of researchers including Johannes Jablonski, Georg-Daniel Schwarz, Philip Heltweg, and Dirk Riehle has published a comprehensive systematic review on arXiv, titled 'Optimization Opportunities for Cloud-Based Data Pipeline Infrastructures.' The paper, submitted under the Distributed, Parallel, and Cluster Computing category, analyzes existing literature to build an integrated framework for optimizing cloud data pipelines. It establishes a formal theory of optimization goals, focusing on critical trade-offs like cost versus execution time (makespan) and examining architectural dimensions such as single-cloud versus multi-cloud deployments and batch versus stream processing paradigms.

The study's major contribution is identifying significant gaps in current research that hinder practical implementation. The authors highlight that multi-tenant environments—where resources are shared among multiple users or pipelines—are critically underexplored, despite being the standard in cloud computing. Furthermore, they point to a severe lack of evaluation using real industry workloads and data, meaning many proposed optimizations lack validation in production settings. This analysis provides a clear roadmap, urging future research to address these specific, high-impact areas to move from theoretical models to tools that can reliably reduce cost and latency for enterprises running massive data workflows.