In recent years, cloud computing has been widely used. Cloud computing refers to the centralized computing resources, users through the access to the centralized resources to complete the calculation, the cloud computing center will return the results of the program processing to the user. Cloud computing is not only for individual users, but also for enterprise users. By purchasing a cloud server, users do not have to buy a large number of computers, saving computing costs. According to a report by China Economic News Network, the scale of cloud computing in China has reached 209.1 billion yuan. At present, the more mature cloud service providers in China are Ali Cloud, Baidu Cloud, Huawei Cloud and so on. Therefore, this paper proposes an innovative approach to solve complex problems in cloud computing resource scheduling and management using machine learning optimization techniques. Through in-depth study of challenges such as low resource utilization and unbalanced load in the cloud environment, this study proposes a comprehensive solution, including optimization methods such as deep learning and genetic algorithm, to improve system performance and efficiency, and thus bring new breakthroughs and progress in the field of cloud computing resource management.Rational allocation of resources plays a crucial role in cloud computing. In the resource allocation of cloud computing, the cloud computing center has limited cloud resources, and users arrive in sequence. Each user requests the cloud computing center to use a certain number of cloud resources at a specific time.

翻译：近年来，云计算已被广泛应用。云计算是指统一集中的计算资源，用户通过访问集中资源完成计算，云计算中心会将程序处理结果返回给用户。云计算不仅面向个人用户，也面向企业用户。用户通过购买云服务器，无需购置大量计算机，从而节省了计算成本。据中国经济新闻网报道，我国云计算规模已达2091亿元。目前，国内较为成熟的云服务提供商包括阿里云、百度云、华为云等。为此，本文提出了一种利用机器学习优化技术解决云计算资源调度与管理中复杂问题的创新方法。通过深入研究云环境中资源利用率低、负载不平衡等挑战，本文提出了一套综合性解决方案，包括深度学习与遗传算法等优化方法，以提升系统性能与效率，从而为云计算资源管理领域带来新的突破与进展。资源的合理配置在云计算中起着至关重要的作用。在云计算资源分配中，云计算中心的云资源有限，用户按顺序到达，每位用户请求云计算中心在特定时间使用一定数量的云资源。