Details of Research Outputs

Wireless network slicing is a promising technology for next-generation networks to provide tailored on-demand services to mobile users. We consider a scheduling policy for wireless network slicing with the aim to maximize the energy efficiency of the network defined as the ratio of long-run average throughput of user requests to the long- run average power consumption. This gives rise to a problem of extremely high computational complexity which prevents direct application of conventional optimization techniques. We propose a scalable priority-based policy, referred to as the Most Energy-Efficient Resource First (MEERF). MEERF is proved to be asymptotically optimal in the special case appropriate for a local wireless environment with highly dense user population and exponentially distributed service time requirement. The robustness of MEERF to different service time distributions is demonstrated by extensive simulations. We present numerically the effectiveness of MEERF %balancing the QoS and relevant power consumption by comparing it with benchmark policies in a more general network with potentially geographically distributed users and infrastructures. The results show that MEERF outperforms the benchmark policies in most of our experiments and achieves up to 52% improvement in terms of energy efficiency.