Adaptive Controller for Dynamic Power and Performance Management in the Virtualized Computing Systems

Abstract

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.

SCM: A method to improve network service layout efficiency with network evolution

“…This method combines particle swarm optimization algorithm and simulated annealing to obtain the placement selection policy of the live VM migration. From the point of VM controller, Wen and et al propose LS-STR [ 25 ], an adaptive controller for data center, based on the least square self-tuning regulator. The method can adjust VM resources dynamically and reduce the energy cost.…”

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

“…[ 6 ]) or minimize power consumption when tracking performance or load balance between VMs (e.g. [ 7 ] ), or optimize a newly defined objective, which integrates performance, power and the balance between different machines(e.g. [ 3 ][ 8 ]).…”

“…Since it can provide a unified framework as well as rigorous controller design and can deal with dynamic and uncertain environment, control theory has been applied more and more to solve the problem. For example, in [ 7 ], the self-tuning regulator(which is an adaptive controller) is utilized to track performance and then optimize the energy assumption based on linear power model; in [ 8 ] the authors designs optimal controller by integrating the SLA function, and introduces a two-level control with one level being faster and the other being slower; in [ 14 ] Kalman filter is introduced to track the CPU utilizations and update the allocations of CPU resources to VMs accordingly; in [ 15 ] PID controller is proposed to manage power consumption and CPU utilization; in [ 16 ] MPC controller is designed; in [ 6 ] both PID controller and MPC are adopted at the same time; in [ 17 ] the authors compare effects of different controllers and find that predictive controller performs better and has some self-learning behavior. Other recent related papers are referred to [ 18 – 22 ].…”