The concept of green storage in cluster computing has recently attracted enormous interest among researchers. Consequently, several energy-efficient solutions, such as multi-speed disks and disk spin down methods, have been proposed to conserve power in storage systems and improve disk access. Some researchers have assessed their proposed solutions via simulations, while others have used real-world experiments. Both methods have advantages and disadvantages. Simulations can more swiftly assess the benefits of energy-efficient solutions, but various measurement errors can arise from procedural shortcomings. For instance, many power simulation tools fail to consider how heat increases the power overhead of disk operations. Some researchers claim that their modeling methods reduce the measurement error to 5% in the single disk model. However, the demand for large-scale storage systems is growing rapidly. Traditional power measurement using a single disk model is unsuited to such systems because of their complex storage architecture and the unpredictability of numerous disks. Consequently, a number of studies have conducted real machine experiments to assess the performance of their solutions in terms of power conservation, but such experiments are time consuming. To address this problem, this study proposes an efficient simulation tool called Benchmark Analysis Software for Energy-efficient Solution (BASE), which can accurately estimate disks' power consumption in large-scale storage systems. We evaluate the performance of BASE on real-world traces of Academia Sinica (Taiwan) and Florida International University. BASE incorporates an analytical method for assessing the reliability of energy-efficient solutions. The analytical results demonstrate that the measurement error of BASE is 2.5% lower than that achieved in real-world experiments involving energy-estimation experiments. Moreover, the results of simulations to assess solution reliability are identical to those obtained through real-world experiments.
|Number of pages||19|
|Journal||Software - Practice and Experience|
|State||Published - 1 May 2016|
- energy-efficient storage
- power simulation
- reliability analysis
- simulation tool