Presents adaptive communication heuristic algorithms for periodic tasks in asynchronous real-time distributed systems. The heuristic algorithms adapt the application to workload changes through trans-node message-level adaptation mechanisms. We present adaptive communication heuristics for IEEE 802.5 token ring networks that support the priority-driven protocol, and for FDDI networks that use the timed token protocol. The heuristic algorithms adapt periodic computations of the application to workload changes by re-prioritizing application messages and by dynamically changing token holding times at processor nodes, respectively. The objective of the heuristics is to minimize (end-to-end) missed deadline ratios of the tasks. We study the performance of the techniques through a combination of benchmarking and simulation. The performance of the heuristics is compared with an adaptive resource management algorithm that performs adaptation by dynamically replicating application processes for load sharing. The experimental results indicate that the adaptive communication strategies outperform the process replication algorithm for load patterns that cause communication latencies to grow faster than execution latencies. Moreover, we also observe that the adaptive communication algorithms perform as good as the process replication algorithm for load patterns that cause execution latencies to grow faster than communication latencies.