Minimizing Age-Upon-Decisions in Bufferless System: Service Scheduling and Decision Interval

The data freshness at decision epochs of time-sensitive applications, e.g., auto-driving vehicles and autonomous underwater robots, is jointly affected by the statistics update process process. This work considers an update-and-decision system with a Poisson-arrival bufferless queue, where updates are delivered processed for making decisions exponential or periodic intervals. We use age-upon-decisions (AuD) to characterize timeliness moments, missing probability specify whether useful decision-making. Our theoretical analyses 1) present average AuDs probabilities systems deterministic intervals under different service time distributions; 2) show that scheduling, achieves lower AuD smaller than uniformly distributed negative exponentially time; 3) prove periodical larger will eventually drop Poisson along increase rate; however, in system. numerical results simulations verify correctness our analyses, demonstrate outperform applying infinite buffer size.