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maryam Amiri

maryam Amiri

Academic rank: Assistant Professor
ORCID: https://orcid.org/0000-0002-7411-9552
Education: PhD.
ScopusId: 57146848900
Faculty: Engineering
Phone: 32625522


New Framework for Disk Access Number Prediction based on Neural Network, fuzzy Classification and Markov Chain
cloud computing; prediction; energy; disk; access number
Researchers maryam Amiri ، Leyli Mohammad-Khanli


Cloud computing could provide computing environment guaranteed by availability and elasticity by virtualization and powerful infrastructures. According to large infrastructure of cloud, their energy efficiency is always one of the main challenges of cloud for cloud cost reduction and green computing. For energy efficiency, the most important step is to predict the next state of cloud resources, disks, accurately. Then, according to next predicted state, we can make an appropriate decision to reduce energy consumption. The goal of this paper is to represent a new method to predict the next disks' state of cloud. Proposed method predicts next state by fuzzy classification, base prediction method and Markov chain. Then, next situation of cloud disks is specified by rules base. Appropriate decisions are proposed to reduce the energy consumption of resources by specifying next state of disks. The results of experiments show the high accuracy of the proposed method in comparison with the similar methods.