摘要
The rapid growth of streaming media applications on the Internet is proposing higher requirements on energy consumption and I/O performance of the storage systems.However,the optimized I/O requests from different initiators will be mixed disorderly when they are reaching the storage system concurrently,which leads to increasing energy consumption.This paper proposes an energy-saving scheduling scheme based on I/O Stream(ES-IOS).The ES-IOS scheme can take the advantage of the I/O characteristics of streaming media and reorganize the mixed and disordered I/O requests into "streams".Technically,The ES-IOS scheme includes two main points,a priority-based weighted stream scheduling algorithm(PWSS) and a regression-fitting-based popularity prediction algorithm(RFPP).The PWSS algorithm can schedule the I/O streams in weighted queue based on priority to limit energy consumption.The priority of each stream is determined by its popularity.According to the I/O access records over a period,the RFPP algorithm can predict the popularity of each stream via regression fitting.Based on the popularities,the PWSS algorithm assigns more continuous service time to the hot streams and reversely less service time to the cold ones.Trace-driven experiments show that the ES-IOS scheme can reduce the energy consumption by 38%and enhance the I/O throughput by 27%approximately.
The rapid growth of streaming media applications on the Internet is proposing higher require- ments on energy consumption and I/O performance of the storage systems. However, the optimized I/O requests from different initiators will be mixed disorderly when they are reaching the storage sys- tem concurrently, which leads to increasing energy consumption. This paper proposes an energy-sav- ing scheduling scheme based on I/O Stream （ES-IOS）. The ES-IOS scheme can take the advantage of the I/O characteristics of streaming media and reorganize the mixed and disordered I/O requests into ＂streams＂. Technically, The ES-IOS scheme includes two main points, a priority-based weigh- ted stream scheduling algorithm （PWSS） and a regression-fitting-based popularity prediction algo- rithm （RFPP）. The PWSS algorithm can schedule the I/O streams in weighted queue based on pri- ority to limit energy consumption. The priority of each stream is determined by its popularity. Ac- cording to the I/O access records over a period, the RFPP algorithm can predict the popularity of each stream via regression fitting. Based on the popularities, the PWSS algorithm assigns more con- tinuous service time to the hot streams and reversely less service time to the cold ones. Trace-driven experiments show that the ES-IOS scheme can reduce the energy consumption by 38% and enhance the I/O throughput by 27% approximately.
基金
Supported by the National High Technology Research and Development Programme of China(No.2011AA01A102)
