A QoS-Aware Data Reconstruction Strategy for a Data Fault-Tolerant Storage System

Recently, many applications and users rely on cloud storage services, such as Google drive, Dropbox, iCloud and Sky drive, to store private files and system data, and cloud storage services must thus be reliable and secure. To increase reliability, previous studies have proposed a variety of erasure coding algorithms for data fault tolerance for use in storage systems. Although these data fault tolerance mechanisms increase data reliability, implementation also increases storage system costs and energy consumption due to data redundancy. However, to date energy-efficient schemes have only been developed based on a RAID architecture, and none have been implemented using an erasure coding algorithm. To address this issue, this study proposes an energy-aware I/O framework with a quality-of-service (QoS) aware data reconstruction scheduler for erasure coding algorithms, called the EEC-scheme. This approach reduces storage system energy consumption and decreases response times for user requests when the system restores failed disks. A series of experiments show that the proposed scheme can significantly reduce power consumption in storage systems.