GNOSIS: proactive pmage placement using graph neural networks & deep reinforcement learning
Theodoropoulos T., Makris A., Psomakelis E., Carlini E., Mordacchini M., Dazzi P., Tserpes K.
Edge Computing Cloud Computing Component Placement Proactive Image Placement Graph Neural Networks
The transition from Cloud Computing to a Cloud-Edge continuum brings many new exciting possibilities for interactive and data-intensive Next Generation applications, but as many challenges. Approaches and solutions that successfully worked in the Cloud space now need to be rethought for the Edge's distributed, heterogeneous and dynamic ecosystem. The placement of application images needs to be proactively devised to reduce as much as possible the image transfer time and comply with the dynamic nature and strict requirements of the applications. To this end, this paper proposes an approach based on the combination of Graph Neural Networks and actor-critic Reinforcement Learning. The approach is analyzed empirically and compared with a state-of-the-art solution. The results show that the proposed approach exhibits a larger execution times but generally better results in terms of application image placement.
Source: CLOUD 2023 - IEEE 16th International Conference on Cloud Computing, pp. 120–128, Chicago, Illinois, USA, 2-8/7/2023
Publisher: IEEE, New York, USA
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@inproceedings{oai:it.cnr:prodotti:487364,
title = {GNOSIS: proactive pmage placement using graph neural networks \& deep reinforcement learning},
author = {Theodoropoulos T. and Makris A. and Psomakelis E. and Carlini E. and Mordacchini M. and Dazzi P. and Tserpes K.},
publisher = {IEEE, New York, USA},
doi = {10.1109/cloud60044.2023.00022},
booktitle = {CLOUD 2023 - IEEE 16th International Conference on Cloud Computing, pp. 120–128, Chicago, Illinois, USA, 2-8/7/2023},
year = {2023}
}
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