Data Warehouse Workload Management: A Review of Prediction, Optimization, and Resource Allocation Techniques
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Abstract
Data warehouse workload management has become increasingly important due to the growing scale, complexity, and variability of analytical workloads. Different query patterns, varying arrival rates and execution times, huge amounts of data and different resource usage creates challenges to maintaining performance and efficient resource usage. This survey is an overview of the recent technologies used to deal with the workload of data warehouses, focusing on workload prediction, workload optimization and resource allocation. Machine learning, deep learning, and advanced temporal models-based approaches to workload prediction are discussed for workload pattern identification and workload forecasting. The review also covers query optimization, workload scheduling, task prioritization, fragmentation, and workload prediction techniques for optimization. Also studied are resource allocation strategies such as dynamic scaling, workload-aware resource management strategies and proactive allocation of computing resources in cloud-based data warehouse. Survey focuses on the main approaches and their characteristics, including their objectives, techniques, benefits, limitations, and applicability to dynamic analytical environments. Besides, prediction accuracy, workload variation, scalability, adaptability, computational overhead and cost-efficient resource utilization are discussed. Finally, new research opportunities are identified for more intelligent, adaptive, and integrated workload management frameworks, which involve prediction, optimization, and resource allocation to provide efficient data warehouse operation.
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