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Recently, cloud computing is growing rapidly and contributing to the realization of other technologies. Cloud data centers use a lot of energy to host applications, which leads to high operating costs and increased carbon dioxide emissions into the environment. Dynamic consolidation of virtual machines (VMs) into the minimum number of physical servers is an efficient approach to managing energy consumption in cloud. Virtual machines placement (VMP) is an important problem in the proper consolidation of VMs and is the most common way to improve resource utilization. In VMP problem, VMs are consolidated to minimize the number of active physical servers. This consolidation is done using migration of VMs. After placement, the VMs execute on selected physical servers and the underloaded physical servers are off to optimize energy consumption. Basically, the purpose of VMP is to allocate a set of VMs to a subset of physical servers, taking into account some objectives such as reducing energy consumption and violating the service level agreement (SLA). In this article, an improved teaching-learning based optimization (TLBO) algorithm is formulated to solve VMP as a multi-objective problem, which we call VMP-TLBO. VMP-TLBO can perform optimal placement of migrated VMs to physical servers with the purpose of reducing energy consumption and SLA violation. VMP-TLBO is implemented in MATLAB and the experimental results show that proposed algorithm does not violate SLA and compared to the best equivalence algorithm (ie, PAPSO), it improves energy consumption and SLA violation by 1.8% and 5.6%, respectively. In addition, VMP-TLBO consolidates migrated VMs into the minimum number of physical servers to minimize the number of overloaded hosts as much as possible.

This study examined the effect of CMC interaction on Iranian EFL learners' vocabulary improvement. The study was carried out on the basis of a comparative design and tried to compare CMC with face to-face interactions in the Iranian EFL learners in order to see whether the learners' lexical knowledge improved by the CMC interaction. Participants of the study were advanced learners studying in a language institute. The Oxford placement test was used to determine the Iranian EFL learners' proficiency level and ensure a homogeneous sample. Then, the participants were randomly assigned to one control group (face-to-face interaction) and one experimental group (CMC interaction) in order to compare the effect of CMC on the learners' vocabulary improvement. The learners took a pre-test to select 12 target lexical items, treatment activity to perform information-gap task, and two immediate and delayed post-tests for assessing the acquisition of new lexical items. Yahoo Messenger was used to provide the chat communication. The research provided evidence that there was a significant relationship between the use of CMC interaction and face-to-face interaction with regard to improvement in the learners' vocabulary learning. The result indicated that the learners' vocabulary learning improved more in CMC interaction in comparison to face-to-face interaction. In addition, there was a significant difference in negotiating the meaning of new lexical items through CMC interaction in comparison to face-to-face interaction. Moreover, the results indicated that in terms of signal, the CMC interaction outperformed face-to-face group.

With the growth of using the internet advertising, display error rate has been subsequently increased. As an instance of display error rate, it can be referred to advertisement inappropriate to user demand of modifying wrong advertising display. The most important problem related to marketing and advertising is to absolutely consider advertising true or false. To cope with such a problem, personalized advertising is made with respect to users' profile and behavior in order that accurate internet advertising is selected, and each user receives her/his favorite internet advertising. In this study, we presented a new profile with the internet advertising in an online bookstore to students and gathered their responses. Then, we used decision tree in data mining applications and modeled two separated datasets in two states of with a profile and without a profile. The results obtained for both datasets revealed that users profile can highly influence proper classification of the internet advertising.

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