Advanced ECHMM-based machine learning tools for complex big data applications

We present a novel approach for accurate characterization of workloads, which is relevant in the context of complex big data applications.Workloads are generally described with statistical models and are based on the analysis of resource requests measurements of a running program. In this paper we propose to consider the sequence of virtual memory references generated from a program during its execution as a temporal series, and to use spectral analysis principles to process the sequence. However, the sequence is time-varying, so we employed processing approaches based on Ergodic Continuous Hidden Markov Models (ECHMMs) which extend conventional stationary spectral analysis approaches to the analysis of time-varying sequences. In this work, we describe two applications of the proposed approach: The on-line classification of a running process and the generation of synthetic traces of a given workload. The first step was to show that ECHMMs accurately describe virtual memory sequences; to this goal a different ECHMM was trained for each sequence and the related run-time average process classification accuracy, evaluated using trace driven simulations over a wide range of traces of SPEC2000, was about 82%. Then, a single ECHMM was trained using all the sequences obtained from a given running application; again, the classification accuracy has been evaluated using the same traces and it resulted about 76%. As regards the synthetic trace generation, a single ECHMM characterizing a given application has been used as a stochastic generator to produce benchmarks for spanning a large application space