dynamic power management

2001-11-22 Mehdi Amirijoo 1

Dynamic power management

Introduction Implementation, levels of operation Modeling Power and performance issues regarding

power management Policies Conclusions


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.


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

P o w er M an g er

D ev ic e 1 D ev ic e n. . . . .


Implementation Hardware

– Frequency reduction– Supply voltage– Power shutdown

Software– Mostly used– Most flexible

Operative system power manager (OSPM)– Microsoft’s OnNow– ACPI


Modeling View the system as a set of interacting power-

manageable components (PMCs), controlled by

the power manager (PM).

P o w er m an ag er

O b s er v er C o n tr o lle r

S y s tem

P M C 1

C o m m an dO b s er v a tio n

P M C 2


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.


Modeling Ex. StrongArm SA-1100 processor (Intel)

R u n

S l e e pI dle

P = 4 0 0 m W

P = 5 0 m W P = 0 .1 6 m W

9 0 u s

1 6 0 ms

1 0 u

s1 0 u

s

9 0 us


Power and performance issues.. Power management degrades performance.

R eq u es t

Bu s y

W o r k

R eq u es t

I d le Bu s y

T s d S leep T w u W o rk

T 1 T 2 T 3 T 4

W o r k lo ad

D ev ic e

P o w er s ta te

P o w er


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

000

0000

,max

)()(

TPP

TPET

PP

TPETTTPETP

sw

sbe

sw

sbebesbew


Policies Different categories:

– Predictive– Adaptive– Stochastic

Application dependent Statistical properties Resource requirements


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.


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.

),..,,,...,( 11 knidle

nidle

knbusy

nbusy

npred TTTTfT

benpred TT


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

Thrnbusy TT


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.


Policies - Adaptive Low pass filter [Wu1997] :

11 )1( npred

nidle

npred TTT


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.


Policies - Stochastic (Markov) Stationary (or WSS). Statistical properties do not

depend on the time shift, k.

)1,..,0,,..,(

)1,..,,,..,(

10

10

naaf

nkkaaf

n

n

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).


Policies - Stochastic (Markov)

0 10 .9 5 0 .8 5

0 .0 5

0 .1 5

O n O ffs _ o n : 1 .0s _ o f f : 0 .8

s _ o n : 0 .0s _ o f f : 0 .2

s _ o n : 0 .1s _ o f f : 0 .0

s _ o n : 0 .9s _ o f f : 1 .0


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)


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.


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.

dynamic power management

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