研究边缘系统中信息策略对用户放弃与切换行为的影响，揭示了理论极限与实际差异。
Knowledge vs. Experience: Asymptotic Limits of Impatience in Edge Tenants
- 用马尔可夫估计和在线学习策略对比用户行为
- 背压增大时放弃不可避免，切换成功率趋近于零
- 实际系统中策略差异显著，但极限下结果一致
我们研究了两种信息源——基于闭式马尔可夫的残余等待时间估计器和在线训练的演员-评论家模型——在双 M/M/1 系统中对用户放弃（reneging）和切换（jockeying）行为的影响。理论上，在服务速率不等且总耐心时间有限的条件下，当队列积压趋于无穷时，总等待时间线性增长，导致放弃不可避免，成功切换概率趋于零。此外，在温和的次线性误差条件下，两种信息模型表现出相同的渐近极限（鲁棒性）。通过实证验证了这些极限，并量化了有限积压下的差异。结果显示，学习型与解析型信息源在实际规模下产生不同的延迟、放弃率和瞬态切换行为，但最终收敛至理论预测的相同极限。研究揭示了信息价值在有限场景中重要，而在渐近情形下无关，为低成本、支持切换的系统设计提供了轻量级遥测与决策逻辑依据。
原文摘要 · Abstract (English)
We study how two information feeds, a closed-form Markov estimator of residual sojourn and an online trained actor-critic, affect reneging and jockeying in a dual M/M/1 system. Analytically, for unequal service rates and total-time patience, we show that total wait grows linearly so abandonment is inevitable and the probability of a successful jockey vanishes as the backlog approaches towards infinity. Furthermore, under a mild sub-linear error condition both information models yield the same asymptotic limits (robustness). We empirically validate these limits and quantify finite backlog differences. Our findings show that learned and analytic feeds produce different delays, reneging rates and transient jockeying behavior at practical sizes, but converge to the same asymptotic outcome implied by our theory. The results characterize when value-of-information matters (finite regimes) and when it does not (asymptotics), informing lightweight telemetry and decision-logic design for low-cost, jockeying-aware systems.
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