# Towards AIOps in Edge Computing Environments

arXiv: https://arxiv.org/abs/2102.09001 · HTML: https://mlsanity.com/paper/2102.09001/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 12 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [Survey on Models and Techniques for Root-Cause Analysis](https://mlsanity.com/paper/1701.08546/) (2017)
- [ADEPOS: Anomaly Detection based Power Saving for Predictive Maintenance using Edge Computing](https://mlsanity.com/paper/1811.00873/) (2018)

## Built on by (most cited first, 5 of 5)

- [AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges](https://mlsanity.com/paper/2304.04661/) (2023)
- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things](https://mlsanity.com/paper/2211.07619/) (2022)
- [Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets](https://mlsanity.com/paper/2310.00781/) (2023)
- [Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments](https://mlsanity.com/paper/2211.13729/) (2022)

## Abstract (arXiv)

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.