{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:01:55Z","timestamp":1768413715730,"version":"3.49.0"},"reference-count":46,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Auton. Adapt. Syst."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning (ICPTL)-based model training and (2) cluster-level model (CM) training. These approaches aim to find a tradeoff between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL\u2019s model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model.<\/jats:p>","DOI":"10.1145\/3725736","type":"journal-article","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T14:50:50Z","timestamp":1742568650000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Efficient Training Approaches for Performance Anomaly Detection Models in Edge Computing Environments"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4156-6695","authenticated-orcid":false,"given":"Duneesha","family":"Fernando","sequence":"first","affiliation":[{"name":"Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2831-8526","authenticated-orcid":false,"given":"Maria A.","family":"Rodriguez","sequence":"additional","affiliation":[{"name":"Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0587-997X","authenticated-orcid":false,"given":"Patricia","family":"Arroba","sequence":"additional","affiliation":[{"name":"CCS\u2013Center for Computational Simulation, Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0946-1818","authenticated-orcid":false,"given":"Leila","family":"Ismail","sequence":"additional","affiliation":[{"name":"Intelligent Distributed Computing and Systems Research (INDUCE) Lab, Department of Computer Science and Software Engineering, United Arab Emirates University, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9754-6496","authenticated-orcid":false,"given":"Rajkumar","family":"Buyya","sequence":"additional","affiliation":[{"name":"Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,6,12]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"468","volume-title":"IEEE 17th International Conference on Cloud Computing (CLOUD \u201924)","author":"Akbari Negin","year":"2024","unstructured":"Negin Akbari, Adel N. 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