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In this paper, we propose a load distribution scheme on a per-flow basis over multiple controllers in SDN (Software-Defined Networking). Specifically, when the capacity of a controller reaches a threshold, the controller makes incoming messages be migrated to other controllers to prevent them from being blocked. Analytical results show that our scheme has lower blocking probability and higher controller capacity utilization ratio than the conventional scheme.

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Handover support is one of the important issues in mobile networks to guarantee the quality of service (QoS) requirements for mobile users. Alongside the development of network technologies, handover management to provide service continuity has been researched and applied for the Internet or cellular networks such as 3G/4G/5G. However, each network paradigm provides its own individual handover management system, even though there are different kinds of QoS requirements for various mobile services. This causes inefficient network resource utilization from the network operators’ perspectives. Therefore, this paper proposes a QoS-aware flexible mobility management scheme for software-defined networking (SDN)-based mobile networks. The proposed scheme classifies flows into four classes based on the QoS requirements of services in terms of delay and loss tolerance. According to the classified service characteristics, it provides a differential handover method for each flow class to support efficient network operation without any service degradation by interacting between the forwarding plane nodes and SDN controller. The performance analysis shows that the proposed scheme enables flexible network resource utilization, satisfying the QoS requirements for each class well compared to the conventional schemes that only consider their own individual handover procedure.

Recently, pre-trained language representation models such as bidirectional encoder representations from transformers (BERT) have been performing well in commonsense question answering (CSQA). However, there is a problem that the models do not directly use explicit information of knowledge sources existing outside. To augment this, additional methods such as knowledge-aware graph network (KagNet) and multi-hop graph relation network (MHGRN) have been proposed. In this study, we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers (ALBERT) with knowledge graph information extraction technique. We also propose to applying the novel method, schema graph expansion to recent language models. Then, we analyze the effect of applying knowledge graph-based knowledge extraction techniques to recent pre-trained language models and confirm that schema graph expansion is effective in some extent. Furthermore, we show that our proposed model can achieve better performance than existing KagNet and MHGRN models in CommonsenseQA dataset.