# Edge User Allocation with Dynamic Quality of Service

## TL;DR

This paper addresses the dynamic QoS user allocation problem in edge computing, proposing optimal and heuristic solutions to maximize user experience and resource utilization.

## Contribution

It introduces a generalized dynamic QoS model for edge user allocation and develops both optimal and heuristic algorithms for large-scale instances.

## Key findings

- The proposed algorithms outperform baseline methods in experiments.
- Heuristic solutions are effective for large-scale problems.
- Dynamic QoS consideration improves user experience management.

## Abstract

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.