Feature selection for predicting live migration characteristics of virtual machines

Abstract

Virtual machine migration is widely used in cloud data centers to scale and maintain the stability of cloud services. However, the performance metrics of virtual machine (VM) applications during migration that are set in the Service Level Agreements may deteriorate. Before starting a migration, it is necessary to evaluate the migration characteristics that affect the quality of service. These characteristics are the total migration time and virtual machine downtime, which are random variables that depend on a variety of factors. The prediction is based on the VM monitoring data. In this paper, we select the most suitable factors for forecasting five types of migrations: precopy migration, postcopy migration, and modification of precopy migration such as CPU throttling, data compression, and delta compression of modified memory pages. To do this, we analyzed a dataset that includes data on five types of migrations, approximately 8000 records of each type. Using correlation analysis, the factors that mostly affect the total migration time and the VM downtime are chosen. These characteristics are predicted using machine learning methods such as linear regression and the support vector machine. It is shown that the number of factors can be reduced almost twice with the same quality of the forecast. In general, linear regression provides relatively high accuracy in predicting the total migration time and the duration of virtual machine downtime. At the same time, the observed nonlinearity in the correlations shows that it is advisable to use the support vector machine to improve the quality of the forecast.

A Machine Learning-Based Optimization Approach for Pre-Copy Live Virtual Machine Migration

“…Feature selection [55][56][57] is the process of obtaining a set of relevant features of the data set according to a feature selection criteria. Effective feature selection can enhance learning accuracy, minimize learning time, minimize computational overhead (time and space complexity), and simplify learning outcomes.…”