1 research outputs found
Energy-Aware Disk Storage Management: Online Approach with Application in DBMS
Energy consumption has become a first-class optimization goal in design and
implementation of data-intensive computing systems. This is particularly true
in the design of database management systems (DBMS), which was found to be the
major consumer of energy in the software stack of modern data centers. Among
all database components, the storage system is one of the most power-hungry
elements. In previous work, dynamic power management (DPM) techniques that make
real-time decisions to transition the disks to low-power modes are normally
used to save energy in storage systems. In this paper, we tackle the
limitations of DPM proposals in previous contributions. We introduced a DPM
optimization model integrated with model predictive control (MPC) strategy to
minimize power consumption of the disk-based storage system while satisfying
given performance requirements. It dynamically determines the state of disks
and plans for inter-disk data fragment migration to achieve desirable balance
between power consumption and query response time. Via analyzing our
optimization model to identify structural properties of optimal solutions, we
propose a fast-solution heuristic DPM algorithm that can be integrated in
large-scale disk storage systems for efficient state configuration and data
migration. We evaluate our proposed ideas by running simulations using
extensive set of synthetic workloads based on popular TPC benchmarks. Our
results show that our solution significantly outperforms the best existing
algorithm in both energy savings and response time.Comment: 22 pages, 9 figure