# Game Theory-Based Load-Balancing Algorithms for Small Cells Wireless Backhaul Connections +
###### Abstract
A multi-agent system is embedded into the standard cuckoo algorithm and the multi-agent cuckoo algorithm is obtained and used to solve the considered resource allocation problem. In [24] the authors analyze, using computer simulation, different scheduling schemes for radio resources in heterogeneous networks that integrate 5G, 4G, Wi-Fi, and other wireless technologies. The goal of the study is to determine how to improve the throughput in heterogeneous 5G networks. Game theory and genetic algorithm-based LB mechanisms were also proposed in several papers. In [25] the authors study the bandwidth aggregation on backhaul connections in the context of wireless LANs composed of APs with spare backhaul capacity and APs with a shortage of backhaul capacity. The transfer of transmission capacity between the two categories of backhaul connection is modeled as a matching game with the many-to-one setting. In [26], considering the high demand for computation and communication resources and the heterogeneity of these resources in distributed computer systems, the authors propose a new method for solving the LB problem using game theory and a genetic algorithm. The LB problem is modeled as a non-cooperative game of the system's users, and a genetic algorithm is used to solve the formulated LB game. In [27] the authors consider the problem of mobile users' access to cloud services. To ensure high performance and reliability the integration of centralized cloud computing and distributed edge computing is considered. To meet the latency requirements of some computation-intensive applications the authors propose to divert some of the incoming job requests from overloaded cloudlets to under-loaded neighbor cloudlets and this LB process is modeled as a non-cooperative game. The problem of LB in data centers in the context of cloud computing is considered in [28]. The increase in the number of IoT devices and the increasing interest in real-time analytics services could generate significant imbalances in the load of cloud data centers. The paper proposes a Stackelberg (leader-follower) game model for selecting in a balanced way the physical host for executing each task arriving at the data center. A similar problem of LB in data centers in the context of cloud computing is also considered in [29]. The authors propose two solutions for the LB problem, considered a non-cooperative game among users, based on game theory and metaheuristic algorithms. The authors of [30] consider the problem of LB in distributed systems at a more general level. The LB problem defined has two conflicting objectives: minimize the users' expected response time and minimize the total cost incurred by the users. The authors model the LB problem as a non-cooperative game and propose an algorithm to solve the defined game. Offloading traffic from crowded base stations to APs in a 5G environment is considered in [31]. The authors propose a scheme using game theory and the Stackelberg approach for taking the decisions of traffic offloading of crowded networks. The proposed solution can achieve a more efficient QoS in a networking environment involving various types of data with different QoS requirements. The use of GT concepts in LB algorithms over virtual communications tunnels established between an AP node, which receives the user flows, and an Advanced Gateway node is considered in [32]. The paper proposes several GT-based LB algorithms and compares the performance of these algorithms with that of \"classical\" LB algorithms, being demonstrated that some of the GT-based algorithms can manage most efficiently the transmission resources available in the virtual communication tunnels. The short state of the art presented above shows the use at large scale of GT-based algorithms in solving various load balancing and traffic offloading problems. This is mainly due to the flexibility of GT in modeling various optimization problems and the ability of this class of algorithms to solve efficiently the mentioned optimization problems.