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Survey on Network Virtualization Using OpenFlow: Taxonomy, Opportunities, and Open Issues

( Ahmed Abdelaziz ),( Tan Fong Ang ),( Mehdi Sookhak ),( Suleman Khan ),( Athanasios Vasilakos ),( Chee Sun Liew ),( Adnan Akhunzada ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.10
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  ScienceON
  
  KISS
The popularity of network virtualization has recently regained considerable momentum because of the emergence of OpenFlow technology. It is essentially decouples a data plane from a control plane and promotes hardware programmability. Subsequently, OpenFlow facilitates the implementation of network virtualization. This study aims to provide an overview of different approaches to create a virtual network using OpenFlow technology. The paper also presents the OpenFlow components to compare conventional network architecture with OpenFlow network architecture, particularly in terms of the virtualization. A thematic OpenFlow network virtualization taxonomy is devised to categorize network virtualization approaches. Several testbeds that support OpenFlow network virtualization are discussed with case studies to show the capabilities of OpenFlow virtualization. Moreover, the advantages of popular OpenFlow controllers that are designed to enhance network virtualization is compared and analyzed. Finally, we present key research challenges that mainly focus on security, scalability, reliability, isolation, and monitoring in the OpenFlow virtual environment. Numerous potential directions to tackle the problems related to OpenFlow network virtualization are likewise discussed
2
Implications of deep learning for the automation of design patterns organization

Hussain, Shahid,Keung, Jacky,Khan, Arif Ali,Ahmad, Awais,Cuomo, Salvatore,Piccialli, Francesco,Jeon, Gwanggil,Akhunzada, Adnan Elsevier 2018 Journal of parallel and distributed computing Vol.117 No.-
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Abstract Though like other domains such as email filtering, web page classification, sentiment analysis, and author identification, the researchers have employed the text categorization approach to automate organization and selection of design patterns. However, there is a need to bridge the gap between the semantic relationship between design patterns (i.e. Documents) and the features which are used for the organization of design patterns. In this study, we propose an approach by leveraging a powerful deep learning algorithm named Deep Belief Network (DBN) which learns on the semantic representation of documents formulated in the form of feature vectors. We performed a case study in the context of a text categorization based automated system used for the classification and selection of software design patterns. In the case study, we focused on two main research objectives: 1) to empirically investigate the effect of feature sets constructed through the global filter-based feature selection methods besides the proposed approach, and 2) to evaluate the significant improvement in the classification decision (i.e. Pattern organization) of classifiers using the proposed approach. The adjustment of DBN parameters such as a number of hidden layers, nodes and iteration can aid a developer to construct a more illustrative feature set. The experimental promising results suggest the significance of the proposed approach to construct a more representative feature set and improve the classifier’s performance in terms of organization of design patterns. Highlights <UL> <LI> There is a need to bridge the gap between the semantic relationship between patterns. </LI> <LI> We propose an approach by leveraging a powerful deep learning algorithm named Deep Belief Network (DBN). </LI> <LI> The DBN learns on the semantic representation of documents formulated in the form of feature vectors. </LI> <LI> We performed a case study in the context of a text categorization based automated system. </LI> <LI> The experimental promising results suggest the significance of the proposed approach to construct a more representative feature set. </LI> </UL>