Hybrid Best-Fit Heuristic for Energy Efficient Virtual Machine Placement in Cloud Data Centers

Jangiti, Saikishor; Vijayakumar,; Subramaniyaswamy,

doi:10.4108/eai.13-7-2018.162689

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Hence, the mathematical problem formulation (multiobjective function) (Jangiti et al., 2020) is as follows:

\max U (S, C, R) = \sum_{i = 1}^{m} (\begin{matrix} α_{11} \\ \begin{matrix} β_{12} \\ γ_{13} \end{matrix} \end{matrix}) x_{i j} + \sum_{j = 1}^{n} (\begin{matrix} α_{21} \\ \begin{matrix} β_{22} \\ γ_{23} \end{matrix} \end{matrix}) y_{j k} + \sum_{k = 1}^{o} (\begin{matrix} α_{31} \\ \begin{matrix} β_{32} \\ γ_{33} \end{matrix} \end{matrix}) z_{k l} + \sum_{l = 1}^{p} (\begin{matrix} α_{41} \\ \begin{matrix} β_{42} \\ γ_{43} \end{matrix} \end{matrix}) w_{l m}

subject to the following constraints:

\sum_{i = 1}^{m} \sum_{j = 1}^{n} α_{i j} . x_{i j} (t) \leq C T L_{i} (t)

\sum_{i = 1}^{m} \sum_{j = 1}^{n} β_{i j} . x_{i j} (t) \leq C T L_{i} (t)

\sum_{i = 1}^{m} \sum_{j = 1}^{n}

…”

Section: Mathematical Problem Formulationmentioning

confidence: 99%

“…The conflict‐aware VM placement algorithms within this solution also use the best‐fit heuristic algorithm (Lin and Kernighan, 1978; Burke et al., 2009) to choose the best‐fitting placement. The choice of best‐fit heuristic algorithm is made based on the fact that it is known to offer a near‐optimal solution in a reasonable time (Jangiti et al., 2020). In the context of this research, this algorithm ensures that VMs are placed where they fit best to avoid underutilization of resources.…”

Section: Cbac4c Conflict‐aware Vm Placement Architecturementioning

confidence: 99%

See 1 more Smart Citation

CBAC4C: conflict‐based VM isolation control for cloud computing

Dlamini

Eloff

Venter

et al. 2021

Int Trans Operational Res

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For businesses to benefit from the many opportunities of cloud computing, they must first address a number of security challenges, such as the potential leakage of confidential data to unintended third parties. An inter‐VM (where VM is virtual machine) attack, also known as cross‐VM attack, is one threat through which cloud‐hosted confidential data could be leaked to unintended third parties. An inter‐VM attack exploits vulnerabilities between co‐resident guest VMs that share the same cloud infrastructure. In an attempt to stop such an attack, this paper uses the principles of logical analysis to model a solution that provides physical separation of VMs belonging to conflicting tenants based on their levels of conflict. The derived mathematical model is founded on scientific principles and implemented using four conflict‐aware VM placement algorithms. The resultant algorithms consider a tenant's risk appetite and cost implications. The model offers guidance to VM placement and is validated using a proof of concept. A cloud simulation tool was used to test and evaluate the effectiveness and efficiency of the model. The findings reflect that the introduction of the proposed model introduced a time lag in the time it took to place VM instances. On top of this, it was also discovered that the number and size of the VM instances has an effect on the VM placement performance. The findings further illustrate that the conflict tolerance level of a VM has a direct impact on the time it took to place.

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“…Hence, the mathematical problem formulation (multiobjective function) (Jangiti et al., 2020) is as follows: