  Edge computing was introduced as a technical enabler for the demanding
requirements of new network technologies like 5G. It aims to overcome
challenges related to centralized cloud computing environments by distributing
computational resources to the edge of the network towards the customers. The
complexity of the emerging infrastructures increases significantly, together
with the ramifications of outages on critical use cases such as self-driving
cars or health care. Artificial Intelligence for IT Operations (AIOps) aims to
support human operators in managing complex infrastructures by using machine
learning methods. This paper describes the system design of an AIOps platform
which is applicable in heterogeneous, distributed environments. The overhead of
a high-frequency monitoring solution on edge devices is evaluated and
performance experiments regarding the applicability of three anomaly detection
algorithms on edge devices are conducted. The results show, that it is feasible
to collect metrics with a high frequency and simultaneously run specific
anomaly detection algorithms directly on edge devices with a reasonable
overhead on the resource utilization.
