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Change Point Evaluation in Networking Logs with Periodicity Filtering and Bootstrapping

Efficient operation of networking systems is important from resource utilization, OPEX, and energy consumption perspectives. A major factor in efficient operations is the underlying software that controls the networking hardware or virtualized network functions. Most software in hardware-based networking devices is periodically updated, which may or may not have impact on various aspects of the performance of the device. We consider the issue of change point detection in network performance indicators, aiming to detect when such software updates co-occur with changes to any subset of collected performance metrics. In particular, we study the change point detection problem that arises when the placement in time of firmware changes is known a priori, but the presence of any performance implications is unknown. We focus on evaluating change point detection in operational network equipment log data, and consider diurnal variation suppression approaches. We propose the use of periodicity filtering to remove anomalous data sources, and apply a resampling technique using bootstrapping to determine when a software update has performance implications. Our results show that this automated change point detection approach can locate performance-related changes, and that load normalization appears to be the most sensitive approach to diurnal variation suppression.