Distributed multipliers in MWM for analyzing job arrival processes in massive HPC workload datasets

There are three distributed multipliers multifractal wavelet model (MWM) on network traffic. The multifractal wavelet model ( MWM) recently has been introduced as a good choice to yield long range dependence (LRD) and fractal behavior for a job arrival process for parallel workload analysis. In this paper, based on the Multifractal Wavelet Model (MWM), we choose the three kinds of distributions for the multipliers, namely the symmetric -distribution, a symmetric point-mass distribution, and a hybrid distribution, the influence which distributed multipliers had on the MWM was analyzed with the analysis and comparison between the real job arrival process and the synthesized one. We find that the choice of -distribution wavelet multipliers Aj,k is not necessary. For the job arrival process, we can use different distributed multipliers to control the wavelet energy decay in MWM. We compared three multifractal wavelet models for the job arrival process.We find that the point-mass MWM can well match that of the real process.We find that the hybrid MWM can also well match that of the real process.We find that hybrid MWM has no greater advantage than beta MWM.

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