A hybrid multicast–unicast assignment approach for data distribution management in HLA

• A hybrid multicast–unicast data transmission model for DDM in HLA is presented.
• A new adaptive communication protocol selection strategy is proposed.
• A concept of distance is presented to support the assignment decision.
• The utilization of multicast and unicast communication resources can be higher.

The main focus of data distribution management (DDM) in HLA is to reduce the amount of data received by federates in large-scale distributed simulations. The use of limited multicast resources plays a key role in the performance of DDM. In order to improve the performance of DDM by using communication protocol effectively, a hybrid multicast–unicast data transmission problem and its formal definition are presented, and then a hybrid multicast–unicast assignment approach is proposed. The approach uses a new adaptive communication protocol selection (ACPS) strategy to utilize the advantages of multicast and unicast, avoid their disadvantages, and consider the inter-relationship between connections. It includes the ACPS static assignment algorithm and the ACPS dynamic assignment algorithm, according to the difference between the static connections and the dynamic connections. In our approach, a concept of distance is presented to measure the inter-relationship between connections for multicast and the message redundancy for unicast, which is the core of the two algorithms in order to gather the connections to a multicast group or to balance the use of unicast and multicast for best performance. As a result, our algorithms can more effectively decide whether a new connection should use unicast or multicast communication, and whether adjusting previous assignment result can further improve the performance. In addition, a control mechanism is introduced to deal with connection changes during the dynamic assignment. The experiment results indicate that our algorithms can utilize the multicast and unicast communication resources effectively, as well as can achieve better performance than existing methods in the real running environment.