Optimization by decomposition: A step from hierarchic to non-hierarchic systems

A new, non-hierarchic decomposition is formulated for system optimization that uses system analysis, system sensitivity analysis, temporary decoupled optimizations performed in the design subspaces corresponding to the disciplines and subsystems, and a coordination optimization concerned with the redistribution of responsibility for the constraint satisfaction and design trades among the disciplines and subsystems. The approach amounts to a variation of the well-known method of subspace optimization modified so that the analysis of the entire system is eliminated from the subspace optimization and the subspace optimizations may be performed concurrently

Optimization by decomposition: A step from hierarchic to non-hierarchic systems

A new, non-hierarchic decomposition is formulated for system optimization that uses system analysis, system sensitivity analysis, temporary decoupled optimizations performed in the design subspaces corresponding to the disciplines and subsystems, and a coordination optimization concerned with the redistribution of responsibility for the constraint satisfaction and design trades among the disciplines and subsystems, and a coordination optimization concerned with the redistribution of responsibility for the constraint satisfaction and design trades among the disciplines and subsystems. The approach amounts to a variation of the well-known method of subspace optimization modified so that the analysis of the entire system is eliminated from the subspace optimization and the subspace optimizations may be performed concurrently

Learning a Partitioning Advisor with Deep Reinforcement Learning

Commercial data analytics products such as Microsoft Azure SQL Data Warehouse or Amazon Redshift provide ready-to-use scale-out database solutions for OLAP-style workloads in the cloud. While the provisioning of a database cluster is usually fully automated by cloud providers, customers typically still have to make important design decisions which were traditionally made by the database administrator such as selecting the partitioning schemes. In this paper we introduce a learned partitioning advisor for analytical OLAP-style workloads based on Deep Reinforcement Learning (DRL). The main idea is that a DRL agent learns its decisions based on experience by monitoring the rewards for different workloads and partitioning schemes. We evaluate our learned partitioning advisor in an experimental evaluation with different databases schemata and workloads of varying complexity. In the evaluation, we show that our advisor is not only able to find partitionings that outperform existing approaches for automated partitioning design but that it also can easily adjust to different deployments. This is especially important in cloud setups where customers can easily migrate their cluster to a new set of (virtual) machines