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

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Dynamic power management
Dynamic power management

  • Introduction

  • Implementation, levels of operation

  • Modeling

  • Power and performance issues regarding power management

  • Policies

  • Conclusions

Mehdi Amirijoo


  • 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


  • 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


  • Hardware

    • Frequency reduction

    • Supply voltage

    • Power shutdown

  • Software

    • Mostly used

    • Most flexible

  • Operative system power manager (OSPM)

    • Microsoft’s OnNow

    • ACPI

Mehdi Amirijoo


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

Mehdi Amirijoo


  • 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.

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  • Ex. StrongArm SA-1100 processor (Intel)

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Power and performance issues
Power and performance issues..

  • Power management degrades performance.

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Power and performance issues1
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


  • Different categories:

    • Predictive

    • Adaptive

    • Stochastic

  • Application dependent

  • Statistical properties

  • Resource requirements

Mehdi Amirijoo

Policies predictive
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

Policies predictive1
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

Policies predictive2
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

Policies adaptive
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.

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Policies adaptive1
Policies - Adaptive

  • Low pass filter [Wu1997] :

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Policies stochastic
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

Policies stochastic markov
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

Policies stochastic markov2
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

Policies stochastic markov3
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


  • 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