No abstract

Managing Cloud resources efficiently necessitates effective policies that assign applications to hardware in a way that they require the least resources possible. Applications are first assigned to virtual machines which are subsequently placed on the most appropriate server host. If a server becomes overloaded, some of its virtual machines are reassigned. This process requires a hotspot detection mechanism in combination with techniques that select the virtual machine(s) to migrate.In this work we introduce two new virtual machine selection policies, Median Migration Time and Maximum Utilisation, and show that they outperform existing approaches on the criteria of minimising energy consumption, service level agreement violations and the number of migrations when combined with different hotspot detection mechanisms. We show that parametrising the the hotspot detection policies correctly has a significant influence on the workload balance of the system.

Summary Private Cloud provides Cloud services with its relatively limited resources compared to public Clouds. Resources in private Clouds should be used energy efficiently. The resource utilization is determined by the assignment of virtual machines (VMs) to hosts. Because of the frequent changes in the resource requests on VMs, the system might become imbalanced, some hosts are overloaded/underloaded. Virtual machine migration is a solution to ease the imbalance problem. Virtual machine migration includes the selection of a VM for migration and decision upon where it should be taken to (mapped). In this work, VM selection and VM mapping are integrated and aimed to ease the imbalance problem for energy efficiency. Moreover, selection and mapping have become adaptive to ease imbalance, while optimizing energy consumption and adaptively responding to changes in the system. Our proposed adaptive mechanism applies Bayesian inference to estimate the likelihood of a VM migration decision, both VM selection for migration and VM mapping, optimizing energy consumption. The proposed mechanism is evaluated on CloudSim using PlanetLab workload on a heterogeneous Cloud. It is demonstrated to reduce energy consumption significantly, (on average) by 116%, while its total execution time is also, (on average) 5.39 times, shorter than the competing state‐of‐the‐art policies.