A Simulated Annealing Combined Genetic Algorithm for Virtual Machine Migration in Cloud Datacenters

Li, Yidong; Meng, Xiangjun; Dong, Hairong

doi:10.1109/dasc-picom-datacom-cyberscitec.2016.108

Cited by 5 publications

References 22 publications

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Selecting services in the cloud: a decision support methodology focused on infrastructure-as-a-service context

et al. 2022

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Growing demand for reduced local hardware infrastructure is driving the adoption of Cloud Computing. In the Infrastructure-as-a-Service model, service providers offer virtualized computational resources in the form of virtual machine instances. The existence of a large variety of providers and instances makes the decision-making process a difficult task for users, especially as factors such as the datacenter location -where the virtual machine is hosted -have a direct influence on the price of instances. The same instance may present price differences when hosted in dif-ferent geographically distributed datacenters and, because of that, the datacenter location needs to be taken into account through the decision-making process. Given this problem, we propose the D-AHP, a methodology to aid decision-making based on Pareto Dominance and Analytic Hierarchy Process (AHP). In the D-AHP, the dominance concept is applied to reduce the number of instances to be compared; the instances selection is based on a set of objectives, while AHP ranks the selected ones from a set of criteria and sub-criteria, among them the datacenter location. The results from case studies show that differences may arise in the results, regarding which instance is more suitable for the user, when considering the datacenter loca-tion as a criterion to choose an instance. This fact highlights the need to consider this factor during the process of migrating applications to the Cloud. In addition, Pareto Dominance applied early over the set of total instances has proved to be efficient, once it significantly reduces the number of instances to be compared and ordered by the AHP by excluding instances with less computational resources and higher cost in the decision-making process, mainly for larger application workloads.

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