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We present two proactive resource allocation algorithms, called DPR and LPR, for satisfying the timeliness requirements of real-time tasks in asynchronous real-time distributed systems. The algorithms are proactive in the sense that they allow application-specified and user-triggered resource allocation by allowing anticipated task workloads to be specified for future time intervals. When proactively triggered, the algorithms allocate resources to maximize the aggregate deadline-satisfied ratio for the future time interval under the anticipated workload. While DPR uses the earliest deadline first scheduling algorithm as the underlying algorithm for process scheduling and packet scheduling, LPR uses a modified least laxity first scheduling algorithm. We show that LPR is computationally more expensive than DPR. Further, our experimental studies reveal that LPR yields a higher deadline-satisfied ratio than DPR.