Abstract
Migration algorithms and conception of data centers have significant impact on cost and service qualities. Researchers are looking to find effective solutions for Virtual Machine (VM) migration in data centers to reduce power consumption under the given quality of service constraints. This paper presents a model for the consistency of VM migration in a data center by Dynamic Bayesian Networks (DBN). The novelty of our work is to construct a DBN according to the probabilistic dependencies between the parameters in order to make decisions about the corresponding placement of VM in the distributed data centers. In addition, to evaluate the proposed model, we add a new scheduler algorithm inspiring from the DBN model into GreenCloud simulator, called MVMM (Minimization of Virtual Machine Migration), which provides a VM score to make decisions about the matching placement of VMs in order to allow a minimization of future VMs migrations. The results reveal that the use of the proposed approach can reduce energy consumption until 8% compared to other scheduler algorithms.
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Kortas, N., Youssef, H. (2020). MVMM: Data Center Scheduler Algorithm for Virtual Machine Migration. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_87
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DOI: https://doi.org/10.1007/978-3-030-15032-7_87
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