Traditional database management systems (DBMSs) running on powerful single-node servers are usually over-provisioned for most of their daily workloads and, because they do not show good-enough energy proportionality, waste a lot of energy while underutilized. A cluster of small (wimpy) servers, where its size can be dynamically adjusted to the current workload, offers better energy characteristics for those workloads. Yet, data migration, necessary to balance utilization among the nodes, is a non-trivial and time-consuming task that may consume the energy saved. For this reason, a sophisticated and easy to adjust partitioning scheme fostering dynamic reorganization is needed. In this paper, we adapt a technique originally created for SMP systems, called physiological partitioning, to distribute data among nodes that allows to easily repartition data without interrupting transactions. We dynamically partition DB tables based on the nodes' utilization and given energy constraints and compare our approach with physical partitioning and logical partitioning methods. To quantify possible energy saving and its conceivable drawback on query runtimes, we evaluate our implementation on an experimental cluster and compare the results w.r.t. performance and energy consumption. Depending on the workload, we can substantially save energy without sacrificing too much performance.

Because energy use of single-server systems is far from being energy proportional, we explore whether or not better energy efficiency may be achieved by a cluster of nodes whose size is dynamically adjusted to the current workload demand. As data-intensive workloads, we submit specific TPC-H queries against a distributed shared-nothing DBMS, where time and energy use are captured by specific monitoring and measurement devices. We configure various static clusters of varying sizes and show their influence on energy efficiency and performance. Further, using an EnergyController and a load-aware scheduler, we verify the hypothesis that energy proportionality can be well approximated by dynamic clusters.

The constant growth of data in all businesses leads to bigger database servers. While peak load times require fast and heavyweight hardware to guarantee performance, idle times are a waste of energy and money. Todays DBMSs have the ability to cluster several servers for performance and fault tolerance. Nevertheless, they do not support dynamic powering of the cluster's nodes based on the current workload.