Entropy Optimization: Shannon Measure of Entropy and its Properties

Fang, Shu‐Cherng; Tsao, Jacob

doi:10.1007/978-0-387-74759-0_161

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Some mathematical approaches such as AHP [49], eigenvector [76], and Delphi [77] are used to calculate the overall evaluation of each decision-maker. In the objective weighting method, the Shannon entropy method [74,78], multi-objective programming [79,80], etc., are used to determine the weight of each criterion. Shannon's entropy plays a significant role in information theory.…”

Section: Entropy Methodsmentioning

confidence: 99%

Optimal virtual machine scheduling in virtualized cloud environment using VIKOR method

2021

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This paper presents a Power and Resource Utilization-Aware Virtual Machine Scheduling (PRUVMS) algorithm for strengthening resource utilization and diminishing the energy consumption of servers in the cloud environment. The PRUVMS algorithm enhances the resource utilization by migrating the VMs from the underloaded/overloaded servers to a normal server, and it reduces the energy consumption by shutting down the underloaded servers after migrating the VMs. For selecting the suitable server for the VM placement, the ranking of the available servers is evaluated. An illustrative example is presented to validate the PRUVMS algorithm. Further, the PRUVMS algorithm is tested on the PlanetLab workload using the Cloud-Sim simulator. The proposed PRUVMS algorithm improves resource utilization by 68.22% and 37.53% and decreases the energy consumption by 35.53% and 31.34% in comparison with PABFD and CAVMP algorithms, respectively. The improvement in computational results shows the acceptability of the proposed scheduling algorithm in the cloud environment.

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Section: Entropy Methodsmentioning

confidence: 99%