Mr. Arif Hussain, Assistant Professor, Institute of Business Studies and Leadership (IBL) Has Successfully Defended his PhD Thesis

Mr. Arif Hussain, Assistant Professor, Institute of Business Studies and Leadership (IBL) has successfully defended his PhD thesis in public defense held in Islamia College Peshawar, and qualified for the award of doctorate degree. He did his research on “The Impact of Corporate Disclosures and Risk Management on the Performance of Banks in Pakistan” under the supervision of Dr. Shahid Jan Kakakhel, Chairman Management Sciences, Islamia College Peshawar. Dr. Shams Urehman of IBMS (University of Agriculture-Peshawar), served as his external examiner. The public defense was attended by a large number of faculty members, industry and students.

Notification from Director Admissions Regarding "Change in Schedule of MPhil/LLM and PhD for Spring 2019 Admissions"

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Dr. Naimat Ullah Khan , Lecturer in Veterinary Medicine, Published a Research Article

Dr. Naimat Ullah Khan , Lecturer in Veterinary Medicine  College of Veterinary Sciences, AWKUM, published a research article in HEC recognized journal having impact factor entitle Epidemiology of Subclinical Mastitis in Dromedary Camels (Camelus dromedarius) of Two Distinct Agro-Ecological Zones of Pakistan  Pakistan J. Zool., vol. 51(2), pp 527-532, 2019. 

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Dr. Mujib ur Rahman, Demonstrator at Department of Economics, Published a Paper

Dr. Mujib ur Rahman, Demonstrator at Department of Economics, Published a Paper, Title “Cost-Benefit Analysis of Cotton Based Handloom Industry in Peshawar Valley".

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Mr. Muhammad Zakarya, Lecturer of Computer Science, Published a Research Paper

Dr. Muhammad Zakarya, Lecturer of Computer Science, Published a Research Paper of Impact Factor 2.239 with Title "Modelling Resource Heterogeneities in Cloud Simulations and Quantifying their Accuracy"

Abstract

Simulations are often used to evaluate the performance of various scheduling and migration techniques in the context of large computing systems such as clouds and datacenters. To ensure that simulations match the real platform as close as possible, plausible assumptions and accurate statistical models are used in designing simulations; and that could also offer accurate results. However, it is not always possible that similar numerical results would also be achievable in a real cloud test-bed. The reason is that a simulator only abstracts a model and, hence, a system; but does not always reflect the real world scenarios. Therefore, the solution of any research problem using numerical simulation (experimentation) is not just to find a result, but also to ensure the quality and accuracy of the estimated results. CloudSim is largely used in the cloud research community to evaluate the performance of various resource allocation and migration policies. However, resources such as CPU, memory and application heterogeneities are not modelled yet. Moreover, its accuracy is rarely addressed. In this paper, we: (i) describe an extension to CloudSim that offers support for resource (CPU) and application heterogeneities; and (ii) demonstrate several techniques that could be used to measure the accuracy of results obtained in simulations, particularly, in the extended CloudSim. Based on our evaluation, we suggest that the accuracy and precision of the extended version of the CloudSim simulator may be as high as  ∼ 98.63% for certain energy and performance efficient resource allocation and consolidation with migration policies in heterogeneous datacenters.

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