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In Wireless Local Area Networks (WLANs), providing seamless mobility and balancing load among Access Points (APs) are challenging issues due to simple signal strength based association and hand-off mechanisms employed at wireless clients. Extensions to Software Defined Networking (SDN) framework for wireless networks could help to address theses issues in an efficient and cost-effective manner with a central view of WLAN at the SDN controller. In this work, we propose a novel load-aware hand-off algorithm for SDN based WLAN systems which considers traffic load of APs in addition to received signal strength at wireless clients to solve load imbalance among APs and offer seamless mobility. We implemented the proposed algorithm on a small-scale prototype testbed and obtained improved network throughput for mobile clients as well as static clients compared to legacy hand-off algorithms used in WLANs.

Due to rapid growth in the use of mobile devices and as a vital carrier of IoT traffic, mobile networks need to undergo infrastructure wide revisions to meet explosive traffic demand. In addition to data traffic, there has been a significant rise in the control signaling overhead due to dense deployment of small cells and IoT devices. Adoption of technologies like cloud computing, Software Defined Networking (SDN) and Network Functions Virtualization (NFV) is impressively successful in mitigating the existing challenges and driving the path towards 5G evolution. However, issues pertaining to scalability, ease of use, service resiliency, and high availability need considerable study for successful roll out of production grade 5G solutions in cloud. In this work, we propose a scalable Cloud Native Solution for Mobility Management Entity (CNS-MME) of mobile core in a production data center based on micro service architecture. The micro services are lightweight MME functionalities, in contrast to monolithic MME in Long Term Evolution (LTE). The proposed architecture is highly available and supports auto-scaling to dynamically scale-up and scale-down required micro services for load balancing. The performance of proposed CNS-MME architecture is evaluated against monolithic MME in terms of scalability, auto scaling of the service, resource utilization of MME, and efficient load balancing features. We observed that, compared to monolithic MME architecture, CNS-MME provides 7% higher MME throughput and also reduces the processing resource consumption by 26%.

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