Using Logistic Regression to Improve Virtual Machines Management in Cloud Computing Systems

Cloud computing (CC) is a computing model that enables its customers to access a shared pool of resources (e.g., storage, network, servers, etc.) through the Internet with a pay-per-use pricing model. Different service models are employed in CC including the Platform-as-a-Service (PaaS) model, in which the costumers request a certain set of resources and the cloud service providers provide these resources in the form of a virtual machine (VM) running on one of the thousands of hosting servers or physical machines (PMs) of a data center. Where to "place" VMs, how to "execute" them and whether there is a need to "move/migrate" them are important decisions that affect the overall resource utilization and power consumption in the hosting data center. VM consolidation is a technique of migrating or consolidating VMs to PMs in order to prevent the PMs from being overloaded or reduce the number of active PMs and increase their utilization. Consolidation techniques measure PM utilization to decide whether to consolidate the VMs running on it or migrate some of them to another PM. This study aims to optimize resource utilization and energy efficiency in cloud data centers by proposing a new Logistic Regression based host overloading prediction technique that can be used by any VM consolidation technique. The new algorithm have been evaluated using a dynamic workload using the CloudSim simulator. The simulation results show that the proposed algorithm outperforms all other known host status prediction techniques.