Precise VM Placement Algorithm Supported by Data Analytic Service

The popularity and commercial use of cloud computing has prompted an increased concern among cloud service providers for both energy efficiency and quality of service. One of the key techniques used for the efficient use of cloud server resources is virtual machine placement. This work introduces a precise VM placement algorithm for power conservation and SLA violation prevention. The mathematical model of the algorithm is supported by a sophisticated data analytic system implemented as a service. The precision of the algorithm is achieved by allowing each individual VM to build, on demand, its own data model over an appropriate time horizon. Thus the data model can reflect the characteristics of resource usage of the VM accurately. The algorithm can communicate synchronously or asynchronously with the data analytic service which is deployed as a cloud-based solution. In the experiments, several advanced data modelling and use forecasting techniques were evaluated. Results from simulation-based experiments show that the VM placement algorithm (supported by the data analytic service) can effectively reduce power consumption, the number of VM migrations, and prevent SLA violation; it also compares favourably with other heuristic