Workload modeling using pseudo2D-HMM

Abstract

In this paper, we present a novel approach for accurate modeling of computer workloads. According to this approach, the sequences of features generated by a program during its execution are considered as time series and are processed with signal processing techniques both for feature extraction and statistical pattern matching. In the feature extraction phase we used spectral analysis for describing the sequence and to retain the important information. In the pattern matching phase we used a simplified form of bidimensional Hidden Markov Model, called pseudo2D-HMM, as Statistical Machine Learning Algorithm. Several processes of the same workload are necessary to obtain a 2D-HMM model of the workload. In this way, the models are obtained in an initial training phase; we developed techniques for on-line workload classification of a running process and for synthetic traces generation. The proposed algorithms is evaluated via trace-driven simulations using the SPEC 2000 workloads. We show that pseudo2D-HMMs accurately describe memory references sequences; the classification accuracy is about 92% with six different workloads.