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TS-EoH: An Edge Server Task Scheduling Algorithm Based on Evolution of Heuristic (2409.09063v1)
Published 4 Sep 2024 in cs.DC and cs.AI
Abstract: 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 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.
- W. Z. Khan, E. Ahmed, S. Hakak, I. Yaqoob, and A. Ahmed, “Edge computing: A survey,” Future Generation Computer Systems, vol. 97, pp. 219–235, 2019.
- M. Xie, L. Cui, J. Liu, W. Guo, and F. Li, “Time fairness-based application offloading in mobile edge computing with individual qos guarantee,” in 2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2023, pp. 10–17.
- J. Feng, Z. Liu, C. Wu, and Y. Ji, “Ave: Autonomous vehicular edge computing framework with aco-based scheduling,” IEEE Transactions on Vehicular Technology, vol. 66, no. 12, pp. 10 660–10 675, 2017.
- Y. Zhao, B. Li, J. Wang, D. Jiang, and D. Li, “Integrating deep reinforcement learning with pointer networks for service request scheduling in edge computing,” Knowledge-Based Systems, vol. 258, p. 109983, 2022.
- F. Liu, T. Xialiang, M. Yuan, X. Lin, F. Luo, Z. Wang, Z. Lu, and Q. Zhang, “Evolution of heuristics: Towards efficient automatic algorithm design using large language model,” in Forty-first International Conference on Machine Learning, 2024.
- B. Romera-Paredes, M. Barekatain, A. Novikov, M. Balog, M. P. Kumar, E. Dupont, F. J. Ruiz, J. S. Ellenberg, P. Wang, O. Fawzi et al., “Mathematical discoveries from program search with large language models,” Nature, vol. 625, no. 7995, pp. 468–475, 2024.
- Z. Wang, P. Li, S. Shen, and K. Yang, “Task offloading scheduling in mobile edge computing networks,” Procedia Computer Science, vol. 184, pp. 322–329, 2021.
- L. Guo, S. Zhao, S. Shen, and C. Jiang, “Task scheduling optimization in cloud computing based on heuristic algorithm,” Journal of networks, vol. 7, no. 3, p. 547, 2012.
- A. Awad, N. El-Hefnawy, and H. Abdel_kader, “Enhanced particle swarm optimization for task scheduling in cloud computing environments,” Procedia Computer Science, vol. 65, pp. 920–929, 2015.
- I. Rahimi, A. H. Gandomi, K. Deb, F. Chen, and M. R. Nikoo, “Scheduling by nsga-ii: Review and bibliometric analysis,” Processes, vol. 10, no. 1, p. 98, 2022.
- S. Hu and G. Li, “Dynamic request scheduling optimization in mobile edge computing for iot applications,” IEEE Internet of Things Journal, vol. 7, no. 2, pp. 1426–1437, 2019.
- M. Yuan, Y. Li, L. Zhang, and F. Pei, “Research on intelligent workshop resource scheduling method based on improved nsga-ii algorithm,” Robotics and Computer-Integrated Manufacturing, vol. 71, p. 102141, 2021.
- J. Wang and D. Li, “Task scheduling based on a hybrid heuristic algorithm for smart production line with fog computing,” Sensors, vol. 19, no. 5, p. 1023, 2019.
- O. Vinyals, M. Fortunato, and N. Jaitly, “Pointer networks,” Advances in neural information processing systems, vol. 28, 2015.
- X. Wu, Y. Zhong, J. Wu, and K. C. Tan, “As-llm: When algorithm selection meets large language model,” arXiv preprint arXiv:2311.13184, 2023.
- D. Shah, M. R. Equi, B. Osiński, F. Xia, B. Ichter, and S. Levine, “Navigation with large language models: Semantic guesswork as a heuristic for planning,” in Conference on Robot Learning. PMLR, 2023, pp. 2683–2699.
- J. Lehman, J. Gordon, S. Jain, K. Ndousse, C. Yeh, and K. O. Stanley, “Evolution through large models,” in Handbook of Evolutionary Machine Learning. Springer, 2023, pp. 331–366.
- Q. Guo, R. Wang, J. Guo, B. Li, K. Song, X. Tan, G. Liu, J. Bian, and Y. Yang, “Connecting large language models with evolutionary algorithms yields powerful prompt optimizers,” arXiv preprint arXiv:2309.08532, 2023.
- J. Lu, X. Guo, X.-g. Zhao, and H. Zhou, “A parallel tasks scheduling algorithm with markov decision process in edge computing,” in Green, Pervasive, and Cloud Computing: 15th International Conference, GPC 2020, Xi’an, China, November 13–15, 2020, Proceedings 15. Springer, 2020, pp. 362–375.
- M. Xu, Z. Fu, X. Ma, L. Zhang, Y. Li, F. Qian, S. Wang, K. Li, J. Yang, and X. Liu, “From cloud to edge: a first look at public edge platforms,” in Proceedings of the 21st ACM internet measurement conference, 2021, pp. 37–53.
- P. Lai, Q. He, M. Abdelrazek, F. Chen, J. Hosking, J. Grundy, and Y. Yang, “Optimal edge user allocation in edge computing with variable sized vector bin packing,” in Service-Oriented Computing: 16th International Conference, ICSOC 2018, Hangzhou, China, November 12-15, 2018, Proceedings 16. Springer, 2018, pp. 230–245.
- R. Chen, L. Cui, Y. Zhang, J. Chen, K. Yao, Y. Yang, C. Yao, and H. Han, “Delay optimization with fcfs queuing model in mobile edge computing-assisted uav swarms: A game-theoretic learning approach,” in 2020 International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2020, pp. 245–250.
- L. Zeng, J. Sun, J. Ma, and Q. Liu, “Task scheduling based on multi-level hashing and hrrn in cloud computing,” in 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). IEEE, 2021, pp. 667–672.
- Z. Peng, D. Cui, J. Zuo, Q. Li, B. Xu, and W. Lin, “Random task scheduling scheme based on reinforcement learning in cloud computing,” Cluster computing, vol. 18, pp. 1595–1607, 2015.
- Z. Zhou, F. Li, H. Zhu, H. Xie, J. H. Abawajy, and M. U. Chowdhury, “An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments,” Neural Computing and Applications, vol. 32, pp. 1531–1541, 2020.
- Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi, and H. Hajishirzi, “Self-instruct: Aligning language models with self-generated instructions,” arXiv preprint arXiv:2212.10560, 2022.
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