Dynamic power management

1. Dynamic power management • Introduction • Implementation, levels of operation • Modeling • Power and performance issues regarding power management • Policies • Conclusions Mehdi Amirijoo

2. Introduction • To provide the requested services and performance levels with a minimum number of active components or a minimum load on such components. • Assume non-uniform workload. • Assume predictability of workload. • Low overhead of caused by power manager; performance and power. Mehdi Amirijoo

3. Introduction • The power manager (PM) implements a control procedure based on observations and assumptions about the workload. • The control procedure is called a policy. • Oracle power manager Mehdi Amirijoo

4. Implementation • Hardware • Frequency reduction • Supply voltage • Power shutdown • Software • Mostly used • Most flexible • Operative system power manager (OSPM) • Microsoft’s OnNow • ACPI Mehdi Amirijoo

5. Modeling • View the system as a set of interacting power-manageable components (PMCs), controlled by the power manager (PM). Mehdi Amirijoo

6. Modeling • Independent PMCs. • Model PMCs as FSMs; PSMs • Transition between states have a cost. • The cost is associated with delay, performance and power loss. • Service providers and service requesters. Mehdi Amirijoo

7. Modeling • Ex. StrongArm SA-1100 processor (Intel) Mehdi Amirijoo

8. Power and performance issues.. • Power management degrades performance. Mehdi Amirijoo

9. Power and performance issues.. • Break-even time Tbe - minimum length of an idle period to save power. Move to sleep state if Tidle > Tbe • T0 : Transition delay (shutdown and wakeup) • E0 : Transition energy • Ps , Pw : Power in sleeping and working states Mehdi Amirijoo

10. Policies • Different categories: • Predictive • Adaptive • Stochastic • Application dependent • Statistical properties • Resource requirements Mehdi Amirijoo

11. Policies - Predictive • Fixed time-out: • Static • Assume that if a device is idle for , it will remain idle for at least Tbe. • If device idle for , change state to sleep. • Time-out  is computed and set off-line. • Very simple to implement. Requires a timer. • Power is wasted in waiting for time-out. • Can cause many under-predictions. • Adaptive version where  is adjusted online. Mehdi Amirijoo

12. Policies - Predictive • Predictive shut-down [Golding 1996]: • Take decisions based observations of past idle and busy times. Take decision as soon as an idle time starts. • The equation f yields a predicted idle time Tpred • Shut down if • Sample data and fit data to a non-linear regression equation f (off-line). • Computation and memory requirements. Mehdi Amirijoo

13. Policies - Predictive • Predictive shut-down [Srivastava 1996] • Take decision based on observing the last busy time. Take decision as soon as an idle time starts. • If change state. • Suitable for devices where short busy periods are followed by long idle periods. L-shape plot diagrams (idle period vs busy periods). • FSMs similar to multibit branch prediction in processors. • Predictive wake-up Mehdi Amirijoo

14. Policies - Adaptive • Static policies are ineffective when the workload is nonstationary or not known in advance. • Time-out revisited: 1. Adapt the time-out . 2. Keep a pool of time-outs and choose the one that will perform best in this context. 3. As above, but assign a weight to each time-out according to how well it will perform relative to an optimum strategy for the last requests. Mehdi Amirijoo

15. Policies - Adaptive • Low pass filter [Wu1997] : Mehdi Amirijoo

16. Policies - Stochastic • Predictive and adaptive policies lack some properties: • They are based on a two state system model. • Parameter tuning can be hard. • Stochastic policies provide a more general and optimal strategies. • Modeled by Markov chains, Pareto. Mehdi Amirijoo

17. Policies - Stochastic (Markov) • A set of states. Probability associated with the transitions. • The solution of the LP produces stationary, randomized (nondeterministic) policy. • Finding the minimum power policy that meets a given performance constraint can be cast as a linear program (LP, solved in polynomial time). • Stationary (or WSS). Statistical properties do not depend on the time shift, k. Mehdi Amirijoo

18. Policies - Stochastic (Markov) Mehdi Amirijoo

19. Policies - Stochastic (Markov) • The policy computed by LP is globally optimum [Puterman 1994]. • However, requires knowledge of the system and its workload statistics in advance. • An adaptive extension [Chung 1999]: • Policy precharacterization (PC) • Parameter learning (PL) • Policy interpolation (PI) Mehdi Amirijoo

20. Policies - Stochastic (Markov) • An adaptive…(cont.) • Two-parameters Markov. Parameters “describe” the current workload. • PC constructs a 2-dim table, addressed by the values of the two parameters. • The table elements contain the optimal policy, identified by the pair. • Parameter learning is performed during operation. • PI is performed to find a policy as a combination of the nearby policies given by the table and the parameters. Mehdi Amirijoo

21. Conclusions • The policies are application dependent and have to be adopted to devices. • Policies based on stochastic control and specially Markov allows a flexible and general design, where all requirements can be incorporated. • Current models are based on observing requests arrivals. A trend in power management is to include higher-level information, particularly software-based information from compilers and OSs. Mehdi Amirijoo