Energy-Aware Disk Storage Management: Online Approach with Application in DBMS

TL;DR

This paper presents an energy-aware disk storage management approach using model predictive control to optimize power consumption and performance in database systems, outperforming existing methods.

Contribution

Introduces a novel DPM optimization model with MPC strategy and a fast heuristic algorithm for efficient disk state management and data migration in large-scale storage systems.

Findings

Significant energy savings compared to existing algorithms.

Improved query response times under synthetic workloads.

Effective balance between power consumption and performance.

Abstract

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.