Resource Allocation of Cloud Application Through Machine Learning: A Case Study

With the rapid development of WEB applications, the demand for dynamically adjusting computing resources based on the load variation is increasing. However, most of the traditional WEB systems have limited ability to respond to load changes. In order to solve the problem, software self-adaptation technology has been applied to the resource management of WEB systems. Many researchers have tried to propose various software self-adaptation models, all of which contain the control loop "Monitor-Analyze-Plan-Execute". However, the "Knowledge-base" of these models is based on predefined strategy or configuration, which increases the complexity of system development and maintenance. In this paper, machine learning is applied to software self-adaptation through a case study, and the "Knowledge-base" is given by machine learning, which greatly reduces the workload of system maintenance and rule configuration. The results show the case based on machine learning can construct the corresponding management strategy according to WEB system runtime status, and conduct software self-adaptive management.