Application of Machine Learning Optimization in Cloud Computing Resource
  Scheduling and Management
Yifan Zhang Bo Liu Yulu Gong Jiaxin Huang Jingyu Xu Weixiang Wan
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.