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Power and Performance Management in Nonlinear Virtualized Computing Systems via Predictive Control.

Wen C, Mu Y - PLoS ONE (2015)

Bottom Line: Furthermore, because of improvement on chips and reduction of idle power, power consumption in modern machines shows significant nonlinearity, making linear power models(which is commonly adopted in previous work) no longer suitable.To deal with this, we build a discrete system state model, in which all control actions and time delay effect are included by state transition and performance and power can be defined on each state.Experiment results show the effectiveness of the controller.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Beihang University, Beijing, China.

ABSTRACT
The problem of power and performance management captures growing research interest in both academic and industrial field. Virtulization, as an advanced technology to conserve energy, has become basic architecture for most data centers. Accordingly, more sophisticated and finer control are desired in virtualized computing systems, where multiple types of control actions exist as well as time delay effect, which make it complicated to formulate and solve the problem. Furthermore, because of improvement on chips and reduction of idle power, power consumption in modern machines shows significant nonlinearity, making linear power models(which is commonly adopted in previous work) no longer suitable. To deal with this, we build a discrete system state model, in which all control actions and time delay effect are included by state transition and performance and power can be defined on each state. Then, we design the predictive controller, via which the quadratic cost function integrating performance and power can be dynamically optimized. Experiment results show the effectiveness of the controller. By choosing a moderate weight, a good balance can be achieved between performance and power: 99.76% requirements can be dealt with and power consumption can be saved by 33% comparing to the case with open loop controller.

No MeSH data available.


State transition graph for a single PM.
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pone.0134017.g003: State transition graph for a single PM.

Mentions: To simplify but without loss of generality, we describe the discrete model as we do in experiment. Let For PMi, Si = [on, vms, freq] to be discrete and taken out of the following 6 typical values:s0=[off,0,],s1=[on,0,],s2=[on,1,1VM on low frequency],s3=[on,1,1VM on high frequency],s4=[on,2,2VMs both on low frequency],s5=[on,2,2VMs both on high frequency]which are abbreviated as Turn off, Turn on, 1VM low frequency, 1VM high frequency, 2VM low frequency, 2VM high frequency respectively. Then, by regulating resource, one state can be transformed to another, as shown in Fig 3, in which each directed line represents a transition between the states with positive time delay. Transition without time delay, such as the transition between s2 and s3, between s4 and s5, are omitted in the figure.


Power and Performance Management in Nonlinear Virtualized Computing Systems via Predictive Control.

Wen C, Mu Y - PLoS ONE (2015)

State transition graph for a single PM.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4520487&req=5

pone.0134017.g003: State transition graph for a single PM.
Mentions: To simplify but without loss of generality, we describe the discrete model as we do in experiment. Let For PMi, Si = [on, vms, freq] to be discrete and taken out of the following 6 typical values:s0=[off,0,],s1=[on,0,],s2=[on,1,1VM on low frequency],s3=[on,1,1VM on high frequency],s4=[on,2,2VMs both on low frequency],s5=[on,2,2VMs both on high frequency]which are abbreviated as Turn off, Turn on, 1VM low frequency, 1VM high frequency, 2VM low frequency, 2VM high frequency respectively. Then, by regulating resource, one state can be transformed to another, as shown in Fig 3, in which each directed line represents a transition between the states with positive time delay. Transition without time delay, such as the transition between s2 and s3, between s4 and s5, are omitted in the figure.

Bottom Line: Furthermore, because of improvement on chips and reduction of idle power, power consumption in modern machines shows significant nonlinearity, making linear power models(which is commonly adopted in previous work) no longer suitable.To deal with this, we build a discrete system state model, in which all control actions and time delay effect are included by state transition and performance and power can be defined on each state.Experiment results show the effectiveness of the controller.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Beihang University, Beijing, China.

ABSTRACT
The problem of power and performance management captures growing research interest in both academic and industrial field. Virtulization, as an advanced technology to conserve energy, has become basic architecture for most data centers. Accordingly, more sophisticated and finer control are desired in virtualized computing systems, where multiple types of control actions exist as well as time delay effect, which make it complicated to formulate and solve the problem. Furthermore, because of improvement on chips and reduction of idle power, power consumption in modern machines shows significant nonlinearity, making linear power models(which is commonly adopted in previous work) no longer suitable. To deal with this, we build a discrete system state model, in which all control actions and time delay effect are included by state transition and performance and power can be defined on each state. Then, we design the predictive controller, via which the quadratic cost function integrating performance and power can be dynamically optimized. Experiment results show the effectiveness of the controller. By choosing a moderate weight, a good balance can be achieved between performance and power: 99.76% requirements can be dealt with and power consumption can be saved by 33% comparing to the case with open loop controller.

No MeSH data available.