基于机器学习的云计算运维自适应调度算法设计与优化

Design and optimization of cloud computing operation and maintenance adaptive scheduling algorithm based on machine learning

随着云计算技术的不断发展,运维工作面临越来越大的挑战。传统的静态调度算法难以适应动态变化的工作负载和资源需求。为解决这一问题,文章提出了一种基于机器学习的自适应调度算法。利用历史数据训练机器学习模型,该算法实现了对未来工作负载的预测,并根据预测结果动态调整了资源分配。同时,通过优化算法参数和模型结构,提升了算法的性能和适应性。实验结果表明,该算法能够有效提高云计算平台的资源利用率和性能表现,具有良好的应用前景和实用性。

With the continuous development of cloud computing technology,operation and maintenance work is facing increasing challenges.Traditional static scheduling algorithms are difficult to adapt to dynamically changing workloads and resource requirements.To address this issue,the article proposes an adaptive scheduling algorithm based on machine learning.This algorithm trains machine learning models using historical data to predict future workloads and dynamically adjusts resource allocation based on the predicted results.Meanwhile,by optimizing algorithm parameters and model structure,the performance and adaptability of the algorithm have been improved.The experimental results show that the algorithm can effectively improve the resource utilization and performance of cloud computing platforms,and has good application prospects and practicality.