Cloud Computing

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    Intelligent System for Selecting Optimum Instructional Style(s) Based on Fuzzy Logic to Develop a Courseware (ISSIDC)
    (2013) Mohamed Fadlalla Elhag; Awad Haj Ali Ahmed
    Instructor’s teaching experience or instructional style (INST) is an essential factor in the knowledge transfer for T-learning [1], so in this paper I tried to cope (INST) in E-learning system as well. Specifically, this paper proposed (Methodological approach based on Fuzzy logic to Select the Optimum Instructional style(s) for Designing a Specific E-learning system” (MFSOI-DSE). The proposed (MFSOI-DSE) extends the fuzzy logic concepts and techniques in E-learning field, explicitly, in selecting an optimum (INST) for developing a specific courseware. In this paper I proposed a new procedure for representing the (INST) in quantitative values instead of qualitative description, using Frame of cognition/knowledge. In this paper, a conducted case study using the proposed procedure has met the selection of INST for developing a specific courseware with an expertise decision. Moreover, the paper specified the best software tools for building an automated system based on the above mentioned procedure in order to facilitate the (MFSOI-DSE).The topic of this paper lies on a multidisciplinary area of research, so it needs a solid background in computer science, Fuzzy logic and Instructional design field. Finally, I hope this paper will improve the quality of the E-learning systems based on IQP (Instructional Quality Profile) [1].
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    Cloud computing versus in-house clusters: a comparative study
    (2014) Omran Malik Orner Awad; Abdelmonim Mohamed Ali Artoli; Awad Hag Ali Ahmed
    Multi-core cloud clusters are best suited environment for academic institutions in the third world countries to gain supercomputing power and enable researchers to inquest new trends in scientific computing with affordable cost and less administrative load. This work aims to analyze the parallelism efficiency of a parallel computational fluid mechanics solver (the lattice Boltzmann method (LBM» on multi-core cloud clusters. This paper demonstrates reliability and cost effectiveness of using tailormade hired cloud clusters as compared to in-house high performance computing architecture. On these clusters we have found that the lattice Boltzmann implementation on a Cartesian grid is fully adaptive, highly flexible and cost effective to use for solving complex large fluid mechanical systems, such as flooding in real-time at a very low cost on leased cluster than in-house ones
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    Stochastic Simulation Efficiency of Parallel CFD Solver on Elastic Cloud Environment
    (Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd, 2014-03) Omran Malik Omer Awad; Awad Hag Ali Ahmed; Abdelmonem M. Ali Artoli
    Computational fluid dynamics applications become crucial for scientist to understand various Natural phenomenon. These applications require high performance computing resources that most small academic institutions cannot afford. Elastic cloud clusters are best suited environment for those small academic institutions to gain high performance computing power and enable researchers to explore new trends in scientific computing with reasonable cost. This work aims to study the parallelism efficiency; in term of communication time and execution time for a highly optimized parallel lattice Boltzmann solver on elastic cloud clusters. On these elastic clusters we have found that the lattice Boltzmann implementation is fully adaptive, highly flexible and cost effective to use for solving complex large fluid mechanical systems.