DPM

1. DPM Dynamic power management

2. DPM Tree

3. Introduction • Dynamic power management (DPM) reduce power consumption of electronic systems • Most common to shut down idle components. • Timeout • Predectiv • Stochastic • OS-directed power management. • Advanced configuration and power interface, ACPI.

4. Timeout • Adaptive timeout. • The ratio between τ and the latest idle period: short -> increase τ, long -> decrease τ. • τ is updated asymmetrically, increase with 1 s or decrease with ½ s. • Change according to the latest busy period : short -> decrease τ, long -> increase τ. • Device dependent timeout. • τ based on the break-even time of the device under control. • C-competitive • If τ is equal to the break-even time this algorithm is proven to be 2-competitive

5. L-shape • Short busy periods are followed by long idle periods • Long busy periods are followed by short idle periods • Problem in the left corner, only short periods

6. Exponentialaverage • Uses both the predicted and the actual lengths of a previous idle period to predict the next idle period • P(n+1) = a*I(n)+(1-a)*p(n) • The constant a has a value between 0 and 1

7. Predictive wakeup and shutdown • The power manager performs a predictive wakeup, even if there is no incoming request. • Hard to comput the right lenght of the idle period • Take the decision to shutdown based on observation of the previous idle- and busy periods • Take the decision to shutdown based on observation of the recent busy period

8. Adaptive disk shutdown • The requests clusters together to sessioner. • Shutdown the hard disk between sessions. • It is hard to decide how long the treshold should be. • An adjustment parameter decide when the disk should shut down • General adaptive algorithm.

9. Service requestor 0,05 0,95 0 1 0,88 0,12 0 1 0 1 0,95 0,05 0,12 0,88 P = Stochastic model • Service requestor with one transition matrix • Service provider with two transition matrices • Queue with four transition matrices • Power manager • Cost metrics

10. W(0) W(1) W(2) W(3) W(4) W(5) …………… W(WS-2) W(WS-1) 1 1 0 0 1 0 ……………… 1 0 Sliding window • Sliding window is based on the stochastic model • It is used for non-stationary service requests • The basic window operation is to shift one slot constantly every time slice • The shutdown decision is evaluated each period, thus causing overhead • Single- or a multi window approach

11. a b c d 1 e 3 2 1 2 3 Learning tree • Adaptive learning tree can control multiple sleeping states • A sequence of idle periods is transformed in to a sequence of discrete events • All leaf nodes are predictions for the next idle period and store the Prediction Confidence Level (PCL)

12. Application OS Kernel PM Device drivers ACPI drivers AML interpreter Table interface BIOS interface Register interface ACPI ACPI tables ACPI BIOS ACPI Registers BIOS Platform hardware Motherboard device Chipset CPU ACPI • ACPI is a uniform HW/SW interface for power management • It specifies an abstract and flexible interface between hardware components

13. Task-based power management • TBPM is a software-centric approach • TBPM uses a two-dimensional data structure, U, and a vector, P • The matrix U stores the relation between devices and requests • To update U the same approach as in exponential average is used • P contains the percentage of CPU time executing task r • P is updated based on sliding window

14. T1 T2 T3 T1 T2 T3 1 1 2 2 3 3 1 1 2 2 3 3 1 1 2 2 time time T idle idle idle Task scheduling • This algorithm uses task scheduling and tries to make as long idle periods as possibly • Every task has a required device set, RDS • This algorithm can also schedule multiple devices