Name
Abstract | ||
|---|---|---|
In the past decade parallel disk systems have been highly scalable and able to alleviate the problem of disk I/O bottleneck, thereby being widely used to support a wide range of data- intensive applications. Optimizing energy consumption in parallel disk systems has strong impacts on the cost of backup power-generation and cooling equipment, because a significant fraction of the operation cost of data centres is due to energy consumption and cooling. Although a variety of parallel disk systems were developed to achieve high performance and energy efficiency, most existing parallel disk systems lack an adaptive way to conserve energy in dynamically changing workload conditions. To solve this problem, we develop an adaptive energy-conserving algorithm, or DCAPS, for parallel disk systems using the dynamic voltage scaling technique that dynamically choose the most appropriate voltage supplies for parallel disks while guaranteeing specified performance (i.e., desired response times) for disk requests. We conduct extensive experiments to quantitatively evaluate the performance of the proposed energy-conserving strategy. Experimental results consistently show that DCAPS significantly reduces energy consumption of parallel disk systems in a dynamic environment over the same disk systems without using the DCAPS strategy. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.future.2012.05.003 | Distributed Simulation and Real-Time Applications |
Keywords | Field | DocType |
data partitioning mechanism,adaptive energy-saving scheme,adaptive energy-conserving mechanism,adaptive energy-conserving strategy,response time estimator,existing parallel disk system,various parallel disk system,energy conservation,dynamic disk scheduling scheme,parallel disk system,disk system,parallel processing,system dynamics,parallel algorithms,energy efficient,power generation,data centre,energy efficiency,adaptive systems | Dynamic voltage scaling,Bottleneck,Efficient energy use,Adaptive system,Parallel algorithm,Computer science,Real-time computing,Energy consumption,Backup,Scalability,Distributed computing | Journal |
Volume | Issue | ISSN |
29 | 1 | 0167-739X |
Citations | PageRank | References |
6 | 0.47 | 37 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mais Nijim | 1 | 144 | 14.08 |
| Xiao Qin | 2 | 1836 | 125.69 |
| Meikang Qiu | 3 | 3722 | 246.98 |
| Kenli Li | 4 | 1389 | 124.28 |