GNOSIS: Proactive Image Placement Using Graph Neural Networks & Deep Reinforcement Learning

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...

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Bibliographic Details
Main Authors: Theodoropoulos, Theodoros, Makris, Antonios, Psomakelis, Evangelos, Carlini, Emanuele, Mordacchini, Matteo, Dazzi, Patrizio, Tserpes, Konstantinos
Format: Conference Proceeding
Language:English
Subjects:
Cloud computing
Component Placement
Edge Computing
Graph Neural Networks
Greedy algorithms
Image edge detection
Integer linear programming
Metaheuristics
Network topology
Proactive Image Placement
Reinforcement learning
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Summary: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.
ISSN:2159-6190
DOI:10.1109/CLOUD60044.2023.00022