A dynamic disk spin-down technique for mobile computing

Abstract

We address the problem of deciding when to spin down the disk of a mobile computer in order to extend battery life. Since one of the most critical resources in mobile computing environments is battery life, good energy conservation methods can dramatically increase the utility of mobile systems. We use a simple and efficient algorithm based on machine learning techniques that has excellent performance in practice. Our experimental results are based on traces collected from HP C2474s disks. Using this data, the algorithm outperforms several algorithms that are theoretically optimal in under various worst-case assumptions, as well as the best fixed time-out strategy. In particular, the algorithm reduces the power consumption of the disk to about half (depending on the disk's properties) of the energy consumed by a one minute fixed time-out. Since the algorithm adapts to usage patterns, it uses as little as 88% of the energy consumed by the best fixed time-out computed in retrospect.

Combining Initial Segments of Lists

“…This is achieved by first doing an exponential update and then mixing in a bit of either the uniform distribution over all experts or the average of all past distributions over the experts [HW98,BW02,GLL05]. In all experimental evaluations that the authors are aware of, it was crucial to extend the online algorithms to the shifting expert case [HLSS00,GWBA02]. It is a tedious but straightforward exercise to mix in a bit of the uniform distribution into the dynamic programming algorithm of Section 4, thus implementing the Fixed Share algorithm from [HW98].…”

Power Modeling of Solid State Disk for Dynamic Power Management Policy Design in Embedded Systems

“…Douglis, et al offer a DPM policy where the time-out (if there is no new access during the time-out, a low power state is entered) is determined adaptively based on the accuracy of previous time-out prediction [8]. Helmhold, et al present a machine learning-based disk spin-down method [9]. Bisson, et al propose an adaptive algorithm to adaptively calculate the time-out for disk spin down utilizing multiple timeout parameters and considering spin-up latency cost [10].…”

“…Thus, during the wakeup period, we mark the power state as an intermediate state of state transition (PState = Transition2Active), set the total power to the idle state power consumption (P total = P idle ), and remove, if any, a future event for transition to a low power state (lines [8][9][10][11][12] Fig. 3.…”

Improving reliability and energy efficiency of disk systems via utilization control

“…studied the dynamic voltage scaling technique with a real-time garbage collection mechanism to reduce the energy dissipation of flash memory storage systems [18]. A dynamic spin down technique for mobile computing was proposed by Helmbold et al [4]. A mathematical model for each Dynamic Voltage Scaling -enabled system is built and their potential in energy reduction is analyzed by Lin Yuan and Gang Qu [13].…”