  Anomaly detection (AD) has been recently employed in the context of edge
cloud computing, e.g., for intrusion detection and identification of
performance issues. However, state-of-the-art anomaly detection procedures do
not systematically consider restrictions and performance requirements inherent
to the edge, such as system responsiveness and resource consumption. In this
paper, we attempt to investigate the performance of change-point based
detectors, i.e., a class of lightweight and accurate AD methods, in relation to
the requirements of edge cloud systems. Firstly, we review the theoretical
properties of two major categories of change point approaches, i.e., Bayesian
and cumulative sum (CUSUM), also discussing their suitability for edge systems.
Secondly, we introduce a novel experimental methodology and apply it over two
distinct edge cloud test-beds to evaluate the performance of such mechanisms in
real-world edge environments. Our experimental results reveal important
insights and trade-offs for the applicability and the online performance of the
selected change point detectors.
