Εργαστηριακός Συνεργάτης στα πλαίσια του Προπτυχιακού Προγράμματος Σπουδών (ΠΠΣ) στα μαθήματα:
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Abstract
The rapidly increasing number of mobile devices and resource-intensive applications poses substantial obstacles to traditional centralized mobile cloud computing, resulting in increased latency and decreased service quality. Edge computing, which places server capabilities at access nodes, provides a promising solution to the aforementioned issues. However, maintaining operational edge service nodes at each access node can be costly and inefficient. We propose and evaluate a scheme that combines a heuristic service node selection algorithm with machine learning based computational load prediction, with the goal of minimizing latency and balancing load among service nodes. Simulation experiments demonstrate that the proposed scheme substantially enhances system performance, paving the way for a more efficient and responsive edge network infrastructure, particularly in 5G and 6G mobile communication environments.
Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or mass reproduced without the explicit permission of the copyright holder.
Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or mass reproduced without the explicit permission of the copyright holder.
Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or mass reproduced without the explicit permission of the copyright holder.