Spin-down Disk Model Not Spinning Spinning & Ready Spinning & Access Spinning & Seek Spinning up Spinning down Inactivity Timeout threshold* Request Trigger: request or predict Predictive

Spin-down Disk Model Not Spinning Spinning & Ready Spinning & Access Spinning & Seek Spinning up Spinning down T out Inactivity Timeout threshold* Request Trigger: request or predict Predictive ~1- 3s delay E transition = P transition * T transition T down T idle P down P spin E transition = P transition * T transition

Energy = S Power i x Time i Reducing Energy Consumption i e power states Energy = S Power i x Time i To reduce energy used for task: –Reduce power cost of power state I through better technology. –Reduce time spent in the higher cost power states. –Amortize transition states (spinning up or down) if significant. P down  T down + 2*E transition + P spin * T out < P spin *T idle T down = T idle - (T transition + T out )

Power Specs IBM Microdrive (1inch) writing 300mA (3.3V) 1W standby 65mA (3.3V).2W IBM TravelStar (2.5inch) read/write 2W spinning 1.8W low power idle.65W standby.25W sleep.1W startup 4.7 W seek 2.3W

Spin-down Disk Model Not Spinning Spinning & Ready Spinning & Access Spinning & Seek Spinning up Spinning down Request Trigger: request or predict Predictive.2W W 2W 2.3W 4.7W

Spin-Down Policies Fixed Thresholds –T out = spin-down cost s.t. 2*E transition = P spin *T out Adaptive Thresholds: T out = f (recent accesses) –Exploit burstiness in T idle Minimizing Bumps (user annoyance/latency) –Predictive spin-ups Changing access patterns (making burstiness) –Caching –Prefetching

Dynamic Spindown Helmbold, Long, Sherrod (MOBICOM96) Dynamically choose a timeout value as function of recent disk activity Based on machine learning techniques ( for all you AI students!) Exploits bursty nature of disk activity Compares to (related previous work) –best fixed timeout with knowledge of entire sequence of accesses –optimal - per access best decision of what to do –competitive algorithms - fixed timeout based on disk characteristics –commonly used fixed timeouts

Metrics Spindown cost (s): T idle (sec.) s.t. 2*E transition = P spin *T idle Seconds of Energy: 1 unit = P spin * 1sec. - P down  1 sec. Joules Energy used by timeout: idle time if idle time < timeout timeout + spindown cost if idle > timeout P spin *T idle ____________ P spin - P down

Energy used by optimal idle time if idle < spindown cost spindown cost otherwise (immediate spindown) Excess energy: Energy used by timeout - Energy used by opt Loss: Excess energy / idle time.

Share Algorithm Learning algorithm: Each trial, set of experts make prediction Weighted average of experts predictions After each trial update weights of experts –reduce weights of misleading experts –share the slashed weights among good experts

Specifics For Disk Spindown timeout calculation Each expert is a fixed timeout value (100) weights w i initially 1/n Learning rate used to slash misleading experts:  –4.0 Share parameter to recover expert  –. 

For each trial Timeout =  w i x i /  w i Slashes weights for next time w i ’ = w i e -  Loss(xi) Shares remaining weights pool =  w i ’ (1- (1-  ) Loss(xi) ) w i ” = (1-  ) Loss(xi) w i ’ + 1/n pool Claim little sensitivity to parameters Behavior: idleness makes timeout slowly get smaller; busy makes timeout jump to longer

Experiments and Results Used traces of HP C2474s disks (not many details about environment - multiuser?) Most idle times in 0-1 sec range, <1sec next most frequent Results: this algorithm beats everything except optimal Avoids inappropriate spindowns