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Evaluating Load Adjusted Learning Strategies for Client Service Levels Prediction from Cloud-hosted Video Servers

Network managers that succeed in improving the accuracy of client video service level predictions, where the video is deployed in a cloud infrastructure, will have the ability to deliver responsive, SLA-compliant service to their customers. Meeting up-time guarantees, achieving rapid first-call resolution, and minimizing time-to-recovery after video service outages will maintain customer loyalty. To date, regression-based models have been applied to generate these predictions for client machines using the kernel metrics of a server cluster. The effect of time-varying loads on cloud-hosted video servers, which arise due to dynamic user requests have not been leveraged to improve prediction using regularized learning algorithms such as the LASSO and Elastic Net and also Random Forest. We evaluate the performance of load-adjusted learning strategies using a number of learning algorithms and demonstrate that improved predictions are achieved irrespective of the learning approach. A secondary benefit of the load-adjusted learning approach is that it reduces the computational cost as long as the load is not constant. Finally, we demonstrate that Random Forest significantly improve the prediction performance produced by the best performing linear regression variant, the Elastic Net.