  In edge computing, edge servers are placed in close proximity to end-users.
App vendors can deploy their services on edge servers to reduce network latency
experienced by their app users. The edge user allocation (EUA) problem
challenges service providers with the objective to maximize the number of
allocated app users with hired computing resources on edge servers while
ensuring their fixed quality of service (QoS), e.g., the amount of computing
resources allocated to an app user. In this paper, we take a step forward to
consider dynamic QoS levels for app users, which generalizes but further
complicates the EUA problem, turning it into a dynamic QoS EUA problem. This
enables flexible levels of quality of experience (QoE) for app users. We
propose an optimal approach for finding a solution that maximizes app users'
overall QoE. We also propose a heuristic approach for quickly finding
sub-optimal solutions to large-scale instances of the dynamic QoS EUA problem.
Experiments are conducted on a real-world dataset to demonstrate the
effectiveness and efficiency of our approaches against a baseline approach and
the state of the art.
