Abstract
Network slicing is an essential technology in 5G and the forthcoming 6G networks. It aims to embed multiple virtual networks, i.e., network slices, on top of a shared substrate network to meet diverse service requirements. While a considerable body of existing research strives to maximize overall profits by meeting the resource demands of the network slices, optimizing their reliability is frequently overlooked. In this paper, we formalize the network slicing problem as a multi-objective optimization problem that aims to maximize total profits and reliability of network slices. To tackle this problem, we propose a new multi-objective optimization approach that improves over the state-of-the-art algorithm, which can achieve good approximate Pareto front results balancing total profits and reliability of network slices. The performance of our proposed method is evaluated on both artificial and real-world network topologies. Experimental results demonstrate the superior performance of our proposed method compared to the baseline algorithm, outperforming the latter in 92% of instances in terms of the Hypervolume (HV) metric.
Original language | English |
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Title of host publication | 2024 IEEE Congress on Evolutionary Computation (CEC) |
Publisher | IEEE |
Number of pages | 8 |
ISBN (Electronic) | 9798350308365 |
DOIs | |
Publication status | E-pub ahead of print - 8 Aug 2024 |
Event | 13th IEEE Congress on Evolutionary Computation, CEC 2024 - Yokohama, Japan, Yokohama, Japan Duration: 30 Jun 2024 → 5 Jul 2024 |
Conference
Conference | 13th IEEE Congress on Evolutionary Computation, CEC 2024 |
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Country/Territory | Japan |
City | Yokohama |
Period | 30/06/24 → 5/07/24 |