减少I/O设备功耗已越来越被嵌入式系统设计者所关注。传统动态功耗管理（DPM）策略在实 际的多任务环境下无法得到预期的节能效果。提出基于堆栈的预测性超时（SBPT）策略。该 策略通过分析任务的调用和堆栈信息来预测任务对I/O设备的访问模式，并采用多请求源（MSR）模型进行多任务的联合预测。然后根据预测结果分组统计，采用超时技术决策。基于实 际负载的仿真实验表明SBPT策略能够适应多任务的应用环境，更稳定更有效地降低了功耗

More embedded system designers pay attention to how to reduce the po wer consumption of I/O devices. Traditional dynamic power management (DPM) polic ies only focus on the device requests, and neglect the application features behi nd the workload. Because of the assumption about the stationary workload, tradit ional DPM policies can not reach their expected goal under the multi-task envir o nment. The paper presents a stack-based predictive timeout strategy (SBPT). It c an predict the access pattern of the device I/O operations by analyzing the ca lling and stack information of tasks and combine predictions of multiple tasks to form the global prediction according to the multiple-service-requester mode l. At last, classify the I/O request by the global prediction and then make the de cision with the timeout technique based on the distribution of the grouped reque sts. An evaluation study of SBPT using the trace-driven simulation is performed. The results show that SBPT can adapt the non-stationary multi-task environment and reduces power consumption more efficiently than other policies.