Database workload forecasting for hybrid transactional/analytical processing scenario
Journal of Big Data, cilt.13, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 13 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1186/s40537-026-01424-6
- Dergi Adı: Journal of Big Data
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Anahtar Kelimeler: DBMS Workload Forecasting, HTAP, LSTM, OLAP, OLTP
- İstanbul Medipol Üniversitesi Adresli: Evet
Özet
Popular approaches to DBMS (Database Management System) management rely on monitoring log files or using third-party statistical tools to observe workload variations, which requires continuous manual intervention from DBAs (Database Administrator) for diagnosis and tuning. Such reactive management is labor-intensive and difficult to scale. To address this limitation, we present a non-intrusive DBMS workload forecasting framework that anticipates future queries and their execution durations directly from SQL-level statistics, rather than from hardware metrics such as CPU or memory utilization. Our method is designed for hybrid transactional/analytical processing (HTAP) workloads, which are increasingly common in data-lakehouse-based applications. Unlike existing approaches that rely on cluster-based workload modeling, we propose a sorting-based, template-level workload forecasting method. By explicitly modeling and forecasting individual SQL templates, our approach replaces coarse-grained cluster representations with fine-grained, semantically meaningful workload units. This design substantially improves model interpretability and controllability, enabling DBAs and system components to directly understand, inspect, and regulate predicted workload patterns at the query-template level.