Virtual Machine Proactive Scaling in Cloud Systems

Although the investment in Cloud Computing incredibly grows in the last few years, the offered technologies for dynamic scaling in Cloud Systems don't satisfy neither nowadays fluky applications (i.e. social networks, web hosting, content delivery) that exploit the power of the Cloud, nor the energy challenges caused by its data-centers. In this work we propose a proactive model based on an application behaviors prediction technique to predict the future workload behavior of the virtual machines (VMs) executed at Cloud hosts. The predicted information can help VMs to dynamically and proactively be adapted to satisfy the provider demands in terms of increasing the utilization and decreasing the power consumption, and to enhance the services in terms of improving the performance with respect to the Quality of Services (QoS) requirements and dynamic changes demands. We have tested the proposed model using Cloud Sim simulator, and the experiments show that our model is able to avoid undesirable situations caused by dynamic changes such as (peak loads, low utilization) and can decrease the losses of energy consumption, overheating, and resources wastage up to 45% on average.