Performance-power ratio not peaks at 100% load.

<p>Performance-power ratio not peaks at 100% load.</p

The architecture and control flow in the virtualized computing system.

<p>The architecture and control flow in the virtualized computing system.</p

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

<div><p>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.</p></div

Adaptive controller for dynamic power and performance management in the virtualized computing systems.

Power and performance management problem in large scale computing systems like data centers has attracted a lot of interests from both enterprises and academic researchers as power saving has become more and more important in many fields. Because of the multiple objectives, multiple influential factors and hierarchical structure in the system, the problem is indeed complex and hard. In this paper, the problem will be investigated in a virtualized computing system. Specifically, it is formulated as a power optimization problem with some constraints on performance. Then, the adaptive controller based on least-square self-tuning regulator(LS-STR) is designed to track performance in the first step; and the resource solved by the controller is allocated in order to minimize the power consumption as the second step. Some simulations are designed to test the effectiveness of this method and to compare it with some other controllers. The simulation results show that the adaptive controller is generally effective: it is applicable for different performance metrics, for different workloads, and for single and multiple workloads; it can track the performance requirement effectively and save the power consumption significantly

State transition graph for a single PM.

<p>State transition graph for a single PM.</p

Configuration of physical machines.

<p>Configuration of physical machines.</p

Performance, power and state transition in one day at R = 50.

<p>Performance, power and state transition in one day at R = 50.</p

Linear model cannot reflect the reduced idle power of new machine.

<p>Linear model cannot reflect the reduced idle power of new machine.</p

Performance and power criteria under different <i>R</i>.

<p>Performance and power criteria under different <i>R</i>.</p

Load B to be completed, using LS-STR.

<p>When Load B is performed the power and performance are traced. Meanwhile the ideal needed resource and the actual resource allocated by the LS-STR controller are both shown in this figure.</p