A Distributed Neural Network Control Approach for Multicast Services

With the ever-increasing number of multicast data applications recently, considerable efforts have been focused on the design of flow control schemes for multicast services. The main difficulties in designing a flow controller for multicast service are caused by heterogeneous multicast receivers, especially those with large propagation delays, since the feedback arriving at the source is somewhat outdated, and can be harmful to the control operations. To attack the above problem, the present paper describes a novel multicast flow control scheme, the so-called proportional, integrative, derivative plus neural network (PIDNN) predictive technique, which consists of two components: the proportional integrative plus derivative (PID) controller and the back propagation BP neural network (BPNN). This network-assisted property is different from the existing control schemes, in that the PIDNN controller can release the irresponsiveness of a multicast flow caused by those long propagation delays from the receivers. By using BPNN for the receivers with longer propagation delay, this active scheme makes the control more responsive to network status. Thus the rate adaptation can be performed in a timely manner, for the sender to respond to network congestion quickly. We analyze the theoretical aspects of the proposed algorithm, show how the control mechanism can be used to design a controller to support multi-rate multicast transmission based on feedback of explicit rates, and verify this matching using simulations. Simulation results demonstrate the efficiency of our scheme in terms of high link utilization, quick response, scalability, high unitary throughput, intra-session fairness and inter-session fairness.