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09-11-2020 | Issue 6/2021

The Journal of Supercomputing 6/2021

A security-aware virtual machine placement in the cloud using hesitant fuzzy decision-making processes

Journal:
The Journal of Supercomputing > Issue 6/2021
Authors:
Sattar Feizollahibarough, Mehrdad Ashtiani
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Abstract

The introduction of cloud computing systems brought with itself a solution for the dynamic scaling of computing resources leveraging various approaches for providing computing power, networking, and storage. On the other hand, it helped decrease the human resource cost by delegating the maintenance cost of infrastructures and platforms to the cloud providers. Nevertheless, the security risks of utilizing shared resources are recognized as one of the major concerns in using cloud computing environments. To be more specific, an intruder can attack a virtual machine and consequently extend his/her attack to other virtual machines that are co-located on the same physical machine. The worst situation is when the hypervisor is compromised in which all the virtual machines assigned to the physical node will be under security risk. To address these issues, we have proposed a security-aware virtual machine placement scheme to reduce the risk of co-location for vulnerable virtual machines. Four attributes are introduced to reduce the aforementioned risk including the vulnerability level of a virtual machine, the importance level of a virtual machine in the given context, the cumulative vulnerability level of a physical machine, and the capacity of a physical machine for the allocation of new virtual machines. Nevertheless, the evaluation of security risks, due to the various vulnerabilities’ nature as well as the different properties of deployment environments is not quite accurate. To manage the precision of security evaluations, it is vital to consider hesitancy factors regarding security evaluations. To consider hesitancy in the proposed method, hesitant fuzzy sets are used. In the proposed method, the priorities of the cloud provider for the allocation of virtual machines are also considered. This will allow the model to assign more weights to attributes that have higher importance for the cloud provider. Eventually, the simulation results for the devised scenarios demonstrate that the proposed method can reduce the overall security risk of the given cloud data center. The results show that the proposed approach can reduce the risk of attacks caused by the co-location of virtual machines up to 41% compared to the existing approaches.

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