Abstract
Recently, a green data center issue has garnered much attention due to the dramatic growth of data in every conceivable industry and application. With high network bandwidth, mobile applications and user clients always backups program/user data in remote data centers. In addition to the data from users, a data center usually employs a data fault-tolerance mechanism to generate redundant data, so as to keep user data from getting lost/error. To preserve numerous data in data centers, a storage system consumes about 27%-35% of the power consumption in a typical data center. Reducing the energy consumption of storage systems, previous studies conserved power in their respective storage systems by switching idle disks to standby/sleep modes. According to research conducted by Google and the IDEMA standard, frequently setting the disk status to standby mode will increase the disk's Annual Failure Rate and reduce its lifespan. However, in most cases, the authors did not analyze the reliability of their solutions. To address the issue, we propose an evaluation function called E3SaRC (Economic Evaluation of Energy saving with Reliability Constraint), which comprehensively evaluates the effects of a energy-efficient solution by considering the cost of hardware failure when applying energy saving schemes. With system reliability and energy-efficient considerations, this study proposes an energy-efficient and reliable storage system that is composed of an energy-efficient storage scheme with a data fault-tolerance algorithm, an adaptive simulation tool and a monitor framework. First of all, because power consumption is the most important issue in this dissertation, we developed a data placement mechanism called CacheRAID based on a Redundant Array of Independent Disks (RAID-5) architecture to mitigate the random access problems that implicitly exist in RAID techniques and thereby reduce the energy consumption of RAID disks. On system reliability issue, CacheRAID applies a control mechanism to the spin-down algorithm. To further enhance system energy-efficiency of the proposed system, an adaptive simulation tool has been proposed to find the best system parameters for CacheRAID by quickly simulating the current workload on storage systems. At the end, the contributions of this dissertation are presented in two parts. In the first part, our experimental results show that the proposed storage system can reduce the power consumption of the conventional software RAID 5 system by 65-80%. Moreover, according to the E3SaRC measurement, the overall saved cost of CacheRAID, is the largest among the systems that we compared. Second, the analytical results demonstrate that the measurement error of the proposed simulation tool is 2.5% lower than that achieved in real-world experiments involving energy estimation experiments. Therefore, the proposed tool can accurately simulate the power consumption of a storage system under different system settings. According to the experimental results, the proposed system can significantly reduce storage system power consumption and increase the system reliability.