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

TL;DR

This paper proposes new edge server placement and scheduling policies that improve robustness against workload uncertainty, reduce server costs, and lower energy consumption in 5G networks.

Contribution

It introduces a novel resource pooling metric and demonstrates its effectiveness through real and synthetic traces, outperforming existing methods.

Findings

Reduces required edge servers by ~25% for low workload rejection.

Decreases energy consumption of edge servers by ~13%.

Enhances robustness against workload bursts.

Abstract

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.