Long range dependent job arrival process and its implications in grid environments

Abstract

Job arrivals can be described as point processes and it is shown that correlations and fractal behavior can be reliably revealed using the count/rate representation. Using real workload data from production Grids, we show that the second order properties such as the autocorrelation function (ACF) and the scaling behavior can be well reconstructed by a Multifractal Wavelet Model (MWM). A so-called controlled-variability integrate-and-fire (CV-InF) algorithm is applied to transform rates into interarrivals so that a full description of the arrival process can be obtained. The additive nature of rates makes it possible to model different patterns separately and aggregate them back to form a unified process. We further quantify the performance impacts of autocorrelated job arrivals in Grid scheduling using model-driven simulation. It is shown that autocorrelations in the arrival processes can cause performance degradation both at the local and the Grid level.