  Power consumption costs takes upto half of operational expenses of
datacenters making power management a critical concern. Advances in processor
technology provide fine-grained control over operating frequency and voltage of
processors and this control can be used to tradeoff power for performance.
Although many power and performance models exist, they have a significant error
margin while predicting the performance of memory or file-intensive tasks and
HPC applications. Our investigations reveal that the prediction error is due in
part to the fact that they do not take frequency AND CPU variations account,
rather they just depend on the CPU by itself.
  In this paper, we empirically derive power and completion time models using
linear regression with CPU utilization and operating frequency as parameters.
We validate our power model on several Intel and AMD processors by predicting
within 2-7% of measured power. We validate our completion time model using five
kernels of NASA Parallel Benchmark suite and five CPU, memory and
file-intensive benchmarks on four heterogeneous systems and predicting within
1-6% of observed performance. We then show how these models can be employed to
realize as much as 15% savings in power while delivering 44% better performance
for applications deployed in a virtualized environment.
