We present a novel approach for accurate character- ization of workloads. Workloads are generally de- scribed with statistical models and are based on the analysis of resource requests measurements of a run- ning program. In this paper we propose to con- sider the sequence of virtual memory references gen- erated from a program during its execution as a tem- poral 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 Mod- els (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 classication of a run- ning process and the generation of synthetic traces of a given workload. The rst step was to show that ECHMMs accurately describe virtual memory se- quences; to this goal a dierent ECHMM was trained for each sequence and the related run-time average process classication 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 classication 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 gen- erator to produce benchmarks for spanning a large application space.
Ergodic Hidden Markov Models for Workload Characterization Problems
Cuzzocrea, Alfredo;
2017-01-01
Abstract
We present a novel approach for accurate character- ization of workloads. Workloads are generally de- scribed with statistical models and are based on the analysis of resource requests measurements of a run- ning program. In this paper we propose to con- sider the sequence of virtual memory references gen- erated from a program during its execution as a tem- poral 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 Mod- els (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 classication of a run- ning process and the generation of synthetic traces of a given workload. The rst step was to show that ECHMMs accurately describe virtual memory se- quences; to this goal a dierent ECHMM was trained for each sequence and the related run-time average process classication 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 classication 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 gen- erator to produce benchmarks for spanning a large application space.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
