Paper accepted: ADApt: Edge Device Anomaly Detection and Microservice Replica Prediction

We are glad that the paper was accepted for publication at ICFEC 2025. ICFEC focuses on innovations in cloud and edge computing, bringing together researchers and practitioners to discuss emerging challenges and solutions.

Title: ADApt: Edge Device Anomaly Detection and Microservice Replica Prediction

Authors: Narges Mehran, Nikolay Nikolov, Radu Prodan, Dumitru Roman, Dragi Kimovski, Frank Pallas, Peter Dorfinger

Venue: 9th IEEE International Conference on Fog and Edge Computing 2025, in conjunction with CCGrid 2025, 19-22 May, 2025 – Tromso, Norway

Abstract: The recent shift towards increasing user microservices in the Edge computing infrastructure brings new orchestration challenges, such as detecting overutilized resources and scaling out overloaded microservices in response to augmenting requests. In this work, we present the ADApt using the monitoring data related to Edge computing resources, detecting the utilization-based anomalies of resources (e.g., CORE or MEM), investigating the scalability in microservices, and adapting the application executions. To reduce the bottleneck while using computing resources, we first explore monitored devices executing microservices with various requirements, 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 other models. Moreover, ADApt is able to estimate the number of replicas for each microservice close to the actual data without any prediction and to reduce the utilization of the device.