Strategic Scheduling of a Live Migration Virtual Machine using Machine Learning: A Review

Abstract

This paper discusses virtual machines (VM) and their use in server technology. This study focuses on the use of machine learning (ML) to schedule live migrations of VMs. The authors conducted a systematic literature review (SLR) to gather evidence of ML research in server migration. The study found that there is a lack of research in this area, and quantitative research can be conducted to explore the potential of ML in terms of server migration. The paper also presents a selection of paper criteria defined for the study, including the exclusion and inclusion criteria, quality assessment, and quantity assessment. The authors retrieved reliable and related papers using the definition of the paper selection criteria and keywords. The SLR method is not discussed in all papers, and the authors want to develop the title into SLR format to produce high-quality papers on live migration VM machine learning. The paper also includes a journal review that discusses the theory of graph team infra and the scheduling algorithm. The authors also present their research questions, which include the definition of virtual machine, live migration, and its application. The paper includes a list of references that discuss various aspects of VMs, including migration strategies, scheduling methods, and self- management of virtual network resources. The authors conclude that there is a need for further research in the area of ML in server migration, and quantitative research can be conducted to explore the potential of ML in this field.