Sparse Regression Model to Predict a Server Load for Dynamic Adjustments of Server Resources

Along with rapid prevalence of Internet of things (IoT) and mobile network services, network function virtualization (NFV) based infrastructure requires dynamic computational resource adjustments in response to time-varying environments (network traffic, resource usage, failure status, etc). To provide agile resource control and adaptiveness, it is effective to predict a virtual server load by means of machine learning technologies for proactive control. In this paper, we propose a regression analysis model utilizing a sparse modeling to predict the average server load in a future specific time period on the basis of the server load in a past specific time period. We target at the number of access to a Web server as a virtual server load. The model can reduce the prediction error compared to a traditional least square method. Besides, we propose two methods to further reduce prediction errors: (A) an algorithm for determining the time period targeted for explanatory variables, and (B) two-stage regression analyses. As a result of MATLAB calculations, we show that application of the sparse modeling can reduce the prediction error by more than 40%, and besides, the above two proposed methods are effective at further reducing the prediction error with a few minutes’ learning time. Our model also contributes to make humans’ posterior analysis easier by making a short list of explanatory variables of data than conventional methods.