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Seamless and fast handover is one of main goals in Long Term Evolution (LTE) in supporting mobility and maintaining user's quality of services. Mobility prediction is a technique to identify future targeted base station in advance, to reduce handover latency, and finally to enhance handover performance in wireless networks. In this paper, mobility prediction via Markov Chains with an input of user's mobility history is proposed as a technique to predict the user's movement in femtocells deployment. The results show that our proposed method predicts better when random data is 50% and above compared to the existing method. We had also analysed the effect of unavailable base station to the accuracy of the prediction in our proposed method. From the analysis, it is found that, the length of time collecting the data for the database effect the prediction accuracy in certain duration.

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Software Defined Networking (SDN) is an emerging networking paradigm that provides more flexibility and adaptability in terms of network definition and control. However, SDN is a logically centralized technology. Therefor the control plane (i.e. controller) scalability in SDN in particular, is also one of the problems that needs further focus. OpenFlow is one of the protocol standards in SDN, which allow the separation of the controller from the forwarding plane. The control plane has an SDN embedded firewall and is able to enforce and monitor the network activity. This firewall can be used to control the throughput. However, it may affect SDN performance. In this paper, throughput will be used as a performance metric to evaluate and assess the firewall impact on two protocols; Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) that passes through the forwarding planes. The evaluations have been verified through simulating the SDN OpenFlow network using MININET. The results show that an implementation of firewall module in SDN creates a significant 36% average drop for TCP and 87% average drop for UDP in the bandwidth which eventually affect the quality of the network and applications.

Software-Defined Networking (SDN) is a new type of technology that embraces high flexibility and adaptability. The applications in SDN have the ability to manage and control networks while ensuring load balancing, access control, and routing. These are considered the most significant benefits of SDN. However, SDN can be influenced by several types of conflicting flows which may lead to deterioration in network performance in terms of efficiency and optimisation. Besides, SDN conflicts occur due to the impact and adjustment of certain features such as priority and action. Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. As a result, this paper presents several machine learning algorithms that include Decision Tree (DT), Support Vector Machine (SVM), Extremely Fast Decision Tree (EFDT) and Hybrid (DT-SVM) for detecting and classifying conflicting flows in SDNs. The EFDT and hybrid DT-SVM algorithms were designed and deployed based on DT and SVM algorithms to achieve improved performance. Using a range flows from 1000 to 100000 with an increment of 10000 flows per step in two network topologies namely, Fat Tree and Simple Tree Topologies, that were created using the Mininet simulator and connected to the Ryu controller, the performance of the proposed algorithms was evaluated for efficiency and effectiveness across a variety of evaluation metrics. The experimental results of the detection of conflict flows show that the DT and SVM algorithms achieve accuracies of 99.27% and 98.53% respectively while the EFDT and hybrid DT-SVM algorithms achieve respective accuracies of 99.49% and 99.27%. In addition, the proposed EFDT algorithm achieves 95.73% accuracy on the task of classification between conflict flow types. The proposed EFDT and hybrid DT-SVM algorithms show a high capability of SDN applications to offer fast detection and classification of conflict flows.

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