# TS-EoH: An Edge Server Task Scheduling Algorithm Based on Evolution of Heuristic

arXiv: https://arxiv.org/abs/2409.09063 · HTML: https://mlsanity.com/paper/2409.09063/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model](https://mlsanity.com/paper/2401.02051/) (2024)
- [EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers](https://mlsanity.com/paper/2309.08532/) (2023)
- [Evolution through Large Models](https://mlsanity.com/paper/2206.08896/) (2022)
- [Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning](https://mlsanity.com/paper/2310.10103/) (2023)
- [From Cloud to Edge: A First Look at Public Edge Platforms](https://mlsanity.com/paper/2109.03395/) (2021)
- [Optimal Edge User Allocation in Edge Computing with Variable Sized Vector Bin Packing](https://mlsanity.com/paper/1904.05553/) (2019)

## Built on by (most cited first, 4 of 4)

- [A Systematic Survey on Large Language Models for Algorithm Design](https://mlsanity.com/paper/2410.14716/) (2024)
- [A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving](https://mlsanity.com/paper/2509.08269/) (2025)
- [Large Language Model-Driven Surrogate-Assisted Evolutionary Algorithm for Expensive Optimization](https://mlsanity.com/paper/2507.02892/) (2025)
- [REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models](https://mlsanity.com/paper/2506.07759/) (2025)

## Abstract (arXiv)

With the widespread adoption of 5G and Internet of Things (IoT) technologies, the low latency provided by edge computing has great importance for real-time processing. However, managing numerous simultaneous service requests poses a significant challenge to maintaining low latency. Current edge server task scheduling methods often fail to balance multiple optimization goals effectively. This paper introduces a novel task-scheduling approach based on Evolutionary Computing (EC) theory and heuristic algorithms. We model service requests as task sequences and evaluate various scheduling schemes during each evolutionary process using Large Language Models (LLMs) services. Experimental results show that our task-scheduling algorithm outperforms existing heuristic and traditional reinforcement learning methods. Additionally, we investigate the effects of different heuristic strategies and compare the evolutionary outcomes across various LLM services.