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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 latencyexperienced by their app users. The edge user allocation (EUA) problemchallenges service providers with the objective to maximize the number ofallocated app users with hired computing resources on edge servers whileensuring their fixed quality of service (QoS), e.g., the amount of computingresources allocated to an app user. In this paper, we take a step forward toconsider dynamic QoS levels for app users, which generalizes but furthercomplicates the EUA problem, turning it into a dynamic QoS EUA problem. Thisenables flexible levels of quality of experience (QoE) for app users. Wepropose an optimal approach for finding a solution that maximizes app users'overall QoE. We also propose a heuristic approach for quickly findingsub-optimal solutions to large-scale instances of the dynamic QoS EUA problem.Experiments are conducted on a real-world dataset to demonstrate theeffectiveness and efficiency of our approaches against a baseline approach andthe state of the art.

Edge User Allocation with Dynamic Quality of Service