TY - GEN
T1 - Optimizing Network Slices
T2 - 2024 International Conference on Future Communications and Networks, FCN 2024
AU - Bouras, Christos
AU - Diasakos, Damianos
AU - Gkamas, Apostolos
AU - Kokkinos, Vasileios
AU - Pouyioutas, Philippos
AU - Prodromos, Nikolaos
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In the realm of 5G networking, the optimization of user allocation through network slicing stands as a critical challenge, with the potential to substantially enhance the Quality of Service (QoS). This study examines three AI-based allocation algorithms - Simulated Annealing, which begins with a Randomized algorithm, Greedy, and Local Search with Hill Climbing - to efficiently distribute network resources. Next, we compare the algorithms for different user densities to understand how well each one can handle the situation at hand in terms of balance in allocation, consumption (time and memory) and complexity. Our research advances beyond conventional allocation techniques by offering different solutions for different needs thus improving QoS through the alignment of user demands with network capacity.
AB - In the realm of 5G networking, the optimization of user allocation through network slicing stands as a critical challenge, with the potential to substantially enhance the Quality of Service (QoS). This study examines three AI-based allocation algorithms - Simulated Annealing, which begins with a Randomized algorithm, Greedy, and Local Search with Hill Climbing - to efficiently distribute network resources. Next, we compare the algorithms for different user densities to understand how well each one can handle the situation at hand in terms of balance in allocation, consumption (time and memory) and complexity. Our research advances beyond conventional allocation techniques by offering different solutions for different needs thus improving QoS through the alignment of user demands with network capacity.
KW - 5G Quality of Service (QoS)
KW - AI-Based Allocation Algorithms
KW - Network Slicing
KW - Resource Optimization
KW - Simulated Annealing
UR - https://www.scopus.com/pages/publications/105007286923
U2 - 10.1109/FCN64323.2024.10984896
DO - 10.1109/FCN64323.2024.10984896
M3 - Conference contribution
AN - SCOPUS:105007286923
T3 - 2024 International Conference on Future Communications and Networks, FCN 2024 - Proceedings
BT - 2024 International Conference on Future Communications and Networks, FCN 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 November 2024 through 22 November 2024
ER -