ADApt: Edge Device Anomaly Detection and Microservice Replica Prediction
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912310403006464 |
|---|---|
| author | Mehran, Narges Nikolov, Nikolay Prodan, Radu Roman, Dumitru Kimovski, Dragi Pallas, Frank Dorfinger, Peter |
| author_facet | Mehran, Narges Nikolov, Nikolay Prodan, Radu Roman, Dumitru Kimovski, Dragi Pallas, Frank Dorfinger, Peter |
| contents | The increased usage of Internet of Things devices at the network edge and the proliferation of microservice-based applications create new orchestration challenges in Edge computing. These include detecting overutilized resources and scaling out overloaded microservices in response to surging requests. This work presents ADApt, an extension of the ADA-PIPE tool developed in the DataCloud project, by monitoring Edge devices, detecting the utilization-based anomalies of processor or memory, investigating the scalability in microservices, and adapting the application executions. To reduce the overutilization bottleneck, we first explore monitored devices executing microservices over various time slots, detecting overutilization-based processing events, and scoring them. Thereafter, based on the memory requirements, ADApt predicts the processing requirements of the microservices and estimates the number of replicas running on the overutilized devices. The prediction results show that the gradient boosting regression-based replica prediction reduces the MAE, MAPE, and RMSE compared to others. Moreover, ADApt can estimate the number of replicas close to the actual data and reduce the CPU utilization of the device by 14%-28%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03698 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ADApt: Edge Device Anomaly Detection and Microservice Replica Prediction Mehran, Narges Nikolov, Nikolay Prodan, Radu Roman, Dumitru Kimovski, Dragi Pallas, Frank Dorfinger, Peter Distributed, Parallel, and Cluster Computing The increased usage of Internet of Things devices at the network edge and the proliferation of microservice-based applications create new orchestration challenges in Edge computing. These include detecting overutilized resources and scaling out overloaded microservices in response to surging requests. This work presents ADApt, an extension of the ADA-PIPE tool developed in the DataCloud project, by monitoring Edge devices, detecting the utilization-based anomalies of processor or memory, investigating the scalability in microservices, and adapting the application executions. To reduce the overutilization bottleneck, we first explore monitored devices executing microservices over various time slots, detecting overutilization-based processing events, and scoring them. Thereafter, based on the memory requirements, ADApt predicts the processing requirements of the microservices and estimates the number of replicas running on the overutilized devices. The prediction results show that the gradient boosting regression-based replica prediction reduces the MAE, MAPE, and RMSE compared to others. Moreover, ADApt can estimate the number of replicas close to the actual data and reduce the CPU utilization of the device by 14%-28%. |
| title | ADApt: Edge Device Anomaly Detection and Microservice Replica Prediction |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.03698 |