Dynamic multiagent load balancing using distributed constraint optimization techniques

Resource management is a key challenge in multiagent systems. It is especially important in dynamic environments where decisions need to be made quickly and when decisions can get obsolete quickly. In wireless local area networks WLANs, resource management includes dynamic channel assignment, dynamic transmit power control and load balancing of WLANs traffic. In this work, we present a novel decentralized framework and a distributed optimization algorithm DLB-SDPOP for load balancing in complex WLANs. Our self-stabilizing algorithm focuses on repairing the network structure instead of reconstructing it when network perturbations occur. It controls the complexity of problem solving by utilizing efficient search and leverages uncertainty to reduce the possibility of reaching myopic solutions. The size and number of inter-agent communication messages are significantly reduced using a communication filtering mechanism. We categorize different scenarios based on key characteristics i.e., load, dynamics and uncertainty in WLANs and compare our algorithm with other state of the art distributed constraint optimization algorithms in each scenario. Our empirical results show that our distributed approach improves WLANs load balancing performance significantly.