  Network slicing is essential for transforming future telecommunication
networks into versatile service platforms, but it also presents challenges for
sustainable network operations. While meeting the requirements of network
slices incurs additional energy consumption compared to non-sliced networks,
operators strive to offer diverse 5G and beyond services while maintaining
energy efficiency. In this study, we address the issue of slice
activation/deactivation to reduce energy consumption while maintaining the user
quality of service (QoS). We employ Deep Contextual Multi-Armed Bandit and
Thompson Sampling Contextual Multi-Armed Bandit agents to make
activation/deactivation decisions for individual clusters. Evaluations are
performed using the NetMob23 dataset, which captures the spatio-temporal
consumption of various mobile services in France. Our simulation results
demonstrate that our proposed solutions provide significant reductions in
network energy consumption while ensuring the QoS remains at a similar level
compared to a scenario where all slice instances are active.
