[论文解读] Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management

Yifan Zhang, Bo Liu+4 more|arXiv (Cornell University)|Feb 27, 2024

Big Data Technologies and Applications被引用 8

一句话总结

论文提出使用机器学习优化技术，包括深度学习和遗传算法，在云计算中提高资源调度和管理，面临低资源利用率和负载不平衡等挑战。

ABSTRACT

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.

研究动机与目标

• 阐明由于资源利用不足和负载不均导致对改进云计算资源调度的需求。
• 提出基于机器学习的优化框架，以提升云数据中心的分配决策。
• 探究深度学习和遗传算法如何提升云资源管理中的系统性能和效率。

提出的方法

• 将深度学习和遗传算法应用为云资源调度的优化方法。
• 针对用户请求的顺序到达和有限的云资源进行资源分配问题。
• 开发一体化方法，以提升云环境中的资源利用率和负载平衡。

实验结果

研究问题

• RQ1 机器学习优化技术能否提高云计算中的资源利用率？
• RQ2 在用户请求按序到达的情况下，深度学习和遗传算法在云资源调度中的表现如何？
• RQ3 基于ML的优化对云资源管理效率有何影响？

主要发现

• 研究提出了一套面向云资源调度与管理的全面ML优化解决方案。
• 该方法针对云环境中资源利用率低和负载不平衡等挑战。
• 该集成方法旨在提升云资源分配的系统性能与效率。