Predictive Load Balancing in Cloud Computing Environments Based on Ensemble Forecasting

Cloud Computing allows the on-demand usage of computing resources in a pay-as-you-use manner. One major problem for Cloud providers is the trade-off between the huge amount of energy consumption resulting from the non optimal utilisation of their servers, and meeting the service level agreements. Virtualisation allows for a better utilisation of existing servers while maintaining the required quality of service, increasing the return on investment. Consequently, dynamic algorithms are needed that determine an optimal plan for the live migration and allocation of Virtual Machines (VM) during run time. The contribution of this paper is as follows: First, we present our forecast module for time series to future utilisation of VMs. Second, we demonstrate how forecasts of CPU utilisation can be used beneficially in Cloud Computing environments. We propose a novel proactive VM migration policy utilising forecasts(PRUF) in Cloud data centres using an predictive overload detection. It uses short-term VM utilisation forecasts based on an ensemble forecasting approach to estimate which host will be overloaded when the next migration process is triggered. A study in the cloud computing simulation toolkit Cloud Sim shows that our predictive approach reduces the number of service level agreement violations and the performance degradation due to VM migrations compared to VM migration algorithms implemented in Cloud Sim.