Predictive Model Resilience in Edge Computing

Wang, Q., Mateo Fornes, J., Anagnostopoulos, C. and Kolomvatsos, K. (2023) Predictive Model Resilience in Edge Computing. In: IEEE 8th World Forum on Internet of Things (WF-IoT2022), Yokohama, Japan, 26 October - 11 November 2022, ISBN 9781665491532 (doi: 10.1109/WF-IoT54382.2022.10152282)

Abstract

Node failure is a commonly seen threat in distributed Machine Learning systems. It is hard to predict having a huge negative impact on system availability to provide e.g., predictive analytics. Considering the benefits obtained from reduced latency and bandwidth overhead in Edge Computing (EC), invocation of the Cloud should be avoided. Hence, finding the best substitute nodes at the network edge to be invoked instead of failing nodes, evidently, builds the system's resilience upon node failures. To achieve this goal, we contribute with a resilience mechanism that relies on several data-mixing strategies that build enhanced models in each node. Such models have satisfactory prediction capabilities to handle failing nodes' predictive tasks, thus, ensuring resilience in predictive services. Furthermore, we propose a graph-driven approach to guide node invocation minimising the performance loss upon node failures. Our performance evaluation and comparative assessment showcase the applicability of our model resilience approach in intelligent EC.

• Item Type: Conference Proceedings
• Additional Information: Jordi Mateo’s work was supported by EU grant Ajuts de requalificacio per a professorat universitari promoted by the Ministeri d’Universitats (NextGenerationEU), Intelligent Energy Europe (IEE) program, and Ministerio de Econom´ıa y Competitividad (PID2020- 113614RB-C22) funded by MCIN/AEI/10.13039/501100011033- Ministerio de Ciencia e Innovacion nad Agencia Estatal de Investigacion. He thanks Glasgow University’s fellowship in the Essence: Data Science & Distributed Computing2 group.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Kolomvatsos, Dr Kostas and Anagnostopoulos, Dr Christos and Mateo Fornes, Dr Jordi and Wang, Mr Qiyuan
• Authors: Wang, Q., Mateo Fornes, J., Anagnostopoulos, C., and Kolomvatsos, K.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781665491532
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in 2022 IEEE 8th World Forum on Internet of Things (WF-IoT)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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