We investigate the dynamics of monitored memory resource utilizations in an operating system under stress using quantitative methods of fractal analysis. In the experiments, we recorded the time series representing various memory related parameters of the operating system. We observed that parameters demonstrate clear multifractal behavior. The degree of fractality of these time series tends to increase as the system workload increases. We conjecture that the Hölder exponent that measures the local rate of fractality may be used as a quantitative measure of software aging. We propose a simple proactive computer crash avoidance strategy based on the online fractal analysis of system memory resource observations.

The discovery of fractal phenomenon in computer-related areas such as network traffic flow leads to the hypothesis that many computer resources display fractal characteristics. The goal of this study is to apply fractal analysis to computer memory usage patterns. We devise methods for calculating the Hölder exponent of a time series and calculating the fractal dimension of a plot of a time series. These methods are then applied to memory-related data collected from a Unix server. We find that our methods for calculating the Hölder exponent of a time series yield results that are independently confirmed through calculation of the fractal dimension of the time series, and that computer memory use does indeed display multifractal behavior. In addition, it is hypothesized that this multifractal behavior may be useful in making certain predictions about the future behavior of an operating system.