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Over the past decade, cloud data management systems became increasingly popular, because they provide on-demand elastic storage and large-scale data analytics in the cloud. These systems were built with the main intention of supporting scalability and availability in an easily maintainable way. However, the (self-) tuning of cloud data management systems to meet specific requirements beyond these basic properties and for possibly heterogeneous applications becomes increasingly complex. Consequently, the self-management ideal of cloud computing is still to be achieved for cloud data management. The focus of this PhD project is (self-) tuning for cloud data management clusters that are serving one of more applications with divergent workload types. It aims to achieve dynamic clustering to support workload based optimization. Our approach is based on logical clustering within a DB cluster based on different criteria such as: data, optimization goal, thresholds, and workload types.