# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
## 1 Introduction  
SDN is a well-known network paradigm which furnishes an encouraging perspective to answer distinct problems in wireless networks [1]. Wi-Fi networks and cellular phones have become common in today's lifestyle universally [2]. The vulnerability of Wi-Fi systems has risen drastically, particularly in communal regions such as shopping malls, universities and airports [3]. The personal and communal Wi-Fi networks are executed in an improvised way and vie for an unlicensed spectrum which can result in a low network efficiency as the APs overlap in coverage due to severe obstruction [3]. In today's dispersed network structure, it is challenging to set up and handle the unplanned installed APs [4]. There is an immediate requirement for coordinated regulation for compact Wi-Fi networks [5]. Cisco-dominated CAPWAP protocols are commonly used to regulate distributed APs infrastructure [6]. SDN is a pioneer network structure designed to create a programmable, effortlessly controllable, and governable network [7]. SDN is logically managed by a centralized network controller that has a comprehensive perspective of the entire network and canfacilitate the implementation of network management protocol [8]. SDN has been recommended to improve Wi-Fi networks and tackle ordinary issues such as obstruction, load balancing, potential management and other related issues [9]. The inaugural configurable southbound interface in SDN, known as OpenFlow, is created for Ethernet switches and does not have provisions for wireless traffic [10]. Numerous studies have used SDN for wireless local area networks (WLANs) [11]. Research has also been conducted to integrate SDN into high density Wi-Fi networks to achieve programmability [12]. In a high density Wi-Fi network scenario, each wireless device (WD) connects with the AP that has the highest RSSI [13]. Thus, many WDs may connect with the same AP with the highest RSSI, while other APs might not be utilized adequately [14]. Such disproportionate traffic patterns may have a substantial adverse effect on network efficacy. At the same time, if numerous users link to the same AP, the contention will be more intense and the throughput will decrease significantly [15]. Across the span of several decades, numerous load balancing strategies have been recommended to improve the association policy in such a way that WDs can select the most suitable AP to connect to the network [16]. The decentralized nature of existing Wi-Fi networks makes it impossible to centrally manage user imbalance distribution [17]. In this paper, we address the load balancing issues in high density Wi-Fi networks by leveraging SDN to boost network performance. By utilizing a network controller that acquires a comprehensive overview of the network, we create and apply load balancing algorithms that incorporate user-side and AP-side data for a symmetrical load balance in the data plane and control plane. In the past the SDN was applied on wired networks and extending it to wireless networks was cumbersome. We set up and test two SDN-based wireless experimental platforms to assess the proposed algorithms using OMNeT++ which supports OpenFlow. The first algorithm makes use of AHP and clustering to enhance load balancing in control plane and the second algorithm makes use of the AP data received by the SDN controller to optimize load on the APs in the data plane. The APs report the information to the SDN controller. In our multi-controller design a UC is designated for overseeing other RCs in terms of processors usage, workload, and response duration. When a new flow arrives at the switch, the switch examines the initial flow packet and categorizes it into various classifications. The switch requests the UC to calculate the built-in relevant flow guidelines. The UC may delegate the appeal to an RC for flow processing. The UC organizes the regional controllers into three categories, including over-utilized clusters (OUC), under-utilized clusters (UUC), and non-utilized clusters (NUC).