  We study how to design edge server placement and server scheduling policies
under workload uncertainty for 5G networks. We introduce a new metric called
resource pooling factor to handle unexpected workload bursts. Maximizing this
metric offers a strong enhancement on top of robust optimization against
workload uncertainty. Using both real traces and synthetic traces, we show that
the proposed server placement and server scheduling policies not only
demonstrate better robustness against workload uncertainty than existing
approaches, but also significantly reduce the cost of service providers.
Specifically, in order to achieve close-to-zero workload rejection rate, the
proposed server placement policy reduces the number of required edge servers by
about 25% compared with the state-of-the-art approach; the proposed server
scheduling policy reduces the energy consumption of edge servers by about 13%
without causing much impact on the service quality.
