Towards AIOps in Edge Computing Environments
This work addresses the problem of operational complexity in edge computing for critical applications like self-driving cars and healthcare, though it is incremental in applying existing anomaly detection methods to a new context.
The paper tackles the challenge of managing complex edge computing infrastructures by designing an AIOps platform that enables high-frequency monitoring and anomaly detection directly on edge devices, showing it is feasible with reasonable resource overhead.
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.