用进化启发式算法优化边缘服务器任务调度，降低延迟。
TS-EoH: An Edge Server Task Scheduling Algorithm Based on Evolution of Heuristic
- 基于进化计算与启发式策略，动态优化任务调度顺序。
- 在多种LLM服务上实验，性能优于传统启发式与强化学习方法。
- 适合研究边缘计算与智能调度的开发者参考。
随着5G与物联网技术的普及，边缘计算提供的低延迟对实时处理至关重要。然而，大量并发服务请求给保持低延迟带来了重大挑战。当前的边缘服务器任务调度方法往往难以有效平衡多个优化目标。本文提出一种基于进化计算（EC）理论和启发式算法的新任务调度方法。我们将服务请求建模为任务序列，并在每次进化过程中利用大语言模型（LLMs）服务评估不同调度方案。实验结果表明，该调度算法在性能上优于现有的启发式方法和传统强化学习方法。此外，我们还研究了不同启发式策略的影响，并比较了在不同LLM服务下的进化结果。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。