Accurate Multicore Processor Power Models for Power-Aware Resource Management
Power management is one of the biggest challenges facing current datacenters. As processors consume the dominant amount of power in computer systems, power management of multi-core processors is extremely significant. An efficient power model that accurately predict the power consumption of a processor is required to develop effective power management techniques. However, this challenge rises with using virtualization and increasing number of cores in the processors. In this paper, the authors analyze power consumption of a multi-core processor; they develop three statistical CPU-Power models based on the number of active cores and average running frequency using a multiple liner regression. Their models are built upon a virtualized server. The models are validated statistically and experimentally. Statistically, their models cover 97% of system variations.