Efficient Auto-scaling for Host Load Prediction through VM migration in Cloud

The expeditious deployment of Cloud applications and services on wide-ranging Cloud Data Centres (CDC) gives rise to the utilization of many resources. Moreover, by the increase in resource utilization, virtualization also greatly impacts achieving desired performance. The major challenges in virtualization are detecting over-utilized or under-utilized hosts at the right time and the proper scaling of Virtual Machines (VM) on the accurate host. Auto-scaling in Cloud Computing allows the service providers to scale up or down the resources automatically and provides on-demand computing power and storage capacities. Effective utilization and autonomous scaling of resources eventually reduce the load, energy consumption, and operating costs. In this paper, an efficient auto-scaling approach for predicting host load through VM migration has been proposed. The ensemble method using different time-series forecasting models has been proposed to forecast the approaching workload on the host. Based on this predicted load, different algorithms have been devised to detect over-utilized and under-utilized hosts and VMs can be migrated. The designed approach has been validated by experimentation on a real-time Google cluster dataset. The proposed technique significantly improves average CPU utilization and reduces over-utilization and under-utilization. It also minimizes response time, service level agreement violations, and the slighter number of migrations and scaling overhead.

Automated summary

This paper proposes an efficient auto-scaling approach for predicting host load through VM migration. The approach uses an ensemble method with different time-series forecasting models to predict the workload on the host. Based on the predicted load, algorithms have been designed to detect and migrate over-utilized and under-utilized hosts, effectively improving resource utilization.