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

arXiv: https://arxiv.org/abs/1904.05553 · HTML: https://mlsanity.com/paper/1904.05553/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [TS-EoH: An Edge Server Task Scheduling Algorithm Based on Evolution of Heuristic](https://mlsanity.com/paper/2409.09063/) (2024)
- [Hybrid Orchestration of Edge AI and Microservices via Graph-based Self-Imitation Learning](https://mlsanity.com/paper/2603.06669/) (2026)

## Abstract (arXiv)

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.