The emergence of cloud-based database systems has intensified the challenge of achieving optimal performance under large-scale workloads. Conventional tuning methods, which rely on human intervention and static heuristics, often struggle with the complexity and diversity of modern cloud environments. This survey offers an in-depth examination of the reinforcement learning (RL) techniques of automated optimisation of cloud database performance. By continuously observing workload behavior and system feedback, RL enables the database engines to become self-adaptive and reduces the need for continuous manual monitoring.
For more than 30 years, the IBM AS/400 (iSeries/IBM i) platform has supported mission-critical operations in retail and banking with unmatched reliability, seamless database management, and high performance. These legacy environments, however, are under tremendous strategic pressure amid the rapid pace of digital transformation, cloud-native architectures, and the demands of the API economy. In this paper, an in-depth, evidence-based approach is presented to modernize enterprise systems built on the AS/400 platform, supported by case studies of 12 retail chains and 8 banking institutions from North America and Europe.
Autonomous Cloud Database Performance Optimization Using Reinforcement Learning in AWS and Snowflake
Raghu Gopa
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The emergence of cloud-based database systems has intensified the challenge of achieving optimal performance under large-scale workloads. Conventional tuning methods, which rely on human intervention and static heuristics, often struggle with the complexity and diversity of modern cloud environments. This survey offers an in-depth examination of the reinforcement learning (RL) techniques of automated optimisation of cloud database performance. By continuously observing workload behavior and system feedback, RL enables the database engines to become self-adaptive and reduces the need for continuous manual monitoring.
Modernizing AS/400-Based Enterprise Systems: A Practical Framework for Retail and Banking Transformation
Shiva Kumar Devasani
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For more than 30 years, the IBM AS/400 (iSeries/IBM i) platform has supported mission-critical operations in retail and banking with unmatched reliability, seamless database management, and high performance. These legacy environments, however, are under tremendous strategic pressure amid the rapid pace of digital transformation, cloud-native architectures, and the demands of the API economy. In this paper, an in-depth, evidence-based approach is presented to modernize enterprise systems built on the AS/400 platform, supported by case studies of 12 retail chains and 8 banking institutions from North America and Europe.