Towards AIOps in Edge Computing Environments

TL;DR

This paper presents a design for an AIOps platform tailored for edge computing environments, demonstrating that high-frequency metrics collection and anomaly detection are feasible directly on edge devices with manageable resource overhead.

Contribution

It introduces a system design for AIOps in heterogeneous edge environments and evaluates the performance of anomaly detection algorithms on edge devices.

Findings

High-frequency metrics collection is feasible on edge devices.

Anomaly detection algorithms can run on edge devices with reasonable overhead.

The platform supports managing complex, distributed edge infrastructures.

Abstract

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.