Asymmetrical and Lower Bounded Support Vector
The successful design and evaluation of autonomous energy optimization techniques requires the availability of a ubiquitous and accurate set of measurement techniques that are cheap and easy to implement. This article discusses an approach for mathematically estimating the wall power as well as the power of the principal functional units (like DRAM) in the server platforms without incurring the cost of hardware instrumentation. Support Vector Regression (SVR) has proven to be an effective tool in real value function estimation. This paper modifies two loss functions, Vapnik's e-insensitive loss function and an insensitive Huber loss function to be asymmetrical in order to limit underestimates.