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

Design of Robust and Efficient Edge Server Placement and Server Scheduling Policies: Extended Version