# Optimal Edge User Allocation in Edge Computing with Variable Sized Vector Bin Packing

## TL;DR

This paper presents an optimal method for allocating edge users in mobile edge computing by modeling the problem as a variable-sized vector bin packing problem and solving it with Lexicographic Goal Programming, leading to improved performance.

## Contribution

Introduces a novel optimal approach using Lexicographic Goal Programming for edge user allocation modeled as a variable-sized vector bin packing problem.

## Key findings

- Proposed method outperforms baseline approaches in experiments.
- Significantly reduces the number of hired edge servers.
- Maximizes the number of allocated end-users while maintaining QoS.

## Abstract

In mobile edge computing, edge servers are geographically distributed around base stations placed near end-users to provide highly accessible and efficient computing capacities and services. In the mobile edge computing environment, a service provider can deploy its service on hired edge servers to reduce end-to-end service delays experienced by its end-users allocated to those edge servers. An optimal deployment must maximize the number of allocated end-users and minimize the number of hired edge servers while ensuring the required quality of service for end-users. In this paper, we model the edge user allocation (EUA) problem as a bin packing problem, and introduce a novel, optimal approach to solving the EUA problem based on the Lexicographic Goal Programming technique. We have conducted three series of experiments to evaluate the proposed approach against two representative baseline approaches. Experimental results show that our approach significantly outperforms the other two approaches.