[Paper Review] 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 Applications8 citations

TL;DR

The paper proposes using machine learning optimization techniques, including deep learning and genetic algorithms, to improve resource scheduling and management in cloud computing amidst challenges like low resource utilization and unbalanced load.

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.

Motivation & Objective

• Motivate the need for improved resource scheduling in cloud computing due to underutilization and uneven load.
• Propose a machine learning-based optimization framework to enhance allocation decisions in cloud centers.
• Investigate how deep learning and genetic algorithms can improve system performance and efficiency in cloud resource management.

Proposed method

• Apply deep learning and genetic algorithms as optimization methods for cloud resource scheduling.
• Address resource allocation with sequential arrival of user requests and limited cloud resources.
• Develop an integrated approach to improve utilization and load balance in cloud environments.

Experimental results

Research questions

• RQ1 Can machine learning optimization techniques improve resource utilization in cloud computing?
• RQ2 How do deep learning and genetic algorithms perform in cloud resource scheduling under sequential user arrivals?
• RQ3 What is the impact of ML-based optimization on cloud resource management efficiency?

Key findings

• The study proposes a comprehensive ML optimization solution for cloud resource scheduling and management.
• The approach targets challenges such as low resource utilization and unbalanced load in cloud environments.
• The integrated method aims to improve system performance and efficiency in cloud resource allocation.