MODE: mix driven on-line resource demand estimation

Abstract

Adaptive performance management solutions often rely on models that require accurate resource demand measures that are estimated in an on-line manner. However it is typically not possible to directly measure resource demands at the abstraction they are needed, e.g., for a software service within an application server that is invoked by a URL. For such cases, linear regression techniques are often used to estimate resource demands. We evaluate the effectiveness of the Least Squares (LSQ) and Least Absolute Deviations (LAD) regression techniques, used extensively by others, as well as Support Vector Regression (SVR) for the purpose of demand estimation. To the best of our knowledge SVR has not yet been evaluated for computer resource demand estimation. We consider the predictive accuracy of these methods for three different real and simulated workloads. Our results demonstrate the importance of tuning the regression parameters of the techniques. We propose an on-line method named Mix Driven On-line Resource Demand Estimation (MODE) that automatically and quickly tunes the regression parameters for LSQ, LAD, and SVR to achieve their best results. The method is novel in that it relies on pre-defined workload mixes with known aggregate demand values to support the tuning exercise. We show that when employed in an on-line manner, tuning with respect to pre-defined mixes is significantly more accurate than the traditional approach of using only step by step data.