Network slicing is a promising technology for realizing the vision of supporting a wide range of services with diverse and heterogeneous service requirements. With network slicing, the network is partitioned into multiple individual dedicated networks tailored and customized for specific services. However, this causes extra energy consumption to reserve resources for each slice. On the other hand, minimizing the energy consumption in radio access network (RAN) may result in increasing the energy consumption in the cloud and the fronthaul due to higher required processing and data transport. Therefore, the energy should be evaluated from an end-to-end perspective. In this study, we address the problem of minimizing the end-to-end energy consumption of a network with network slicing by jointly reserving communication and computation resources among slices. First, we propose an end-to-end delay and energy model for each slice. We take into account the delays and energy consumption of the radio site, midhaul/fronthaul transport, and the cloud site in an Ethernet-based cloud RAN (C-RAN). Then, we formulate a non-convex optimization problem to minimize the total energy consumption of the network by jointly allocating transmission bandwidth and processing resources in the digital unit pool of the cloud, respectively, to each slice. The constraints of the optimization problem are the total delay requirement of each slice, the maximum allowable bandwidth at each radio unit, the maximum rate limitation of the Ethernet links, and the total processing limit of the cloud. To solve the problem optimally, we transform it into a convex quadratic programming problem. The simulation results show that end-to-end network slicing can decrease the total energy consumption of the network compared to only RAN slicing. We also investigate the impact of the 5G numerology on the allocated resources to each slice and the total energy consumption. We show that using mixed numerology depending on the slice type, we can interplay between delay and energy consumption for each slice.