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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.04241 |
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Table of Contents:
- We study how queue-state information disclosures affect impatient tenants in multi-tenant edge systems. We propose an information-bulletin strategy in which each queue periodically broadcasts two Markov models. One is a model of steady-state service-rate behavior and the other a model of the queue length inter-change times. Tenants autonomously decide to renege or jockey based on this information. The queues observe tenant responses and adapt service rates via a learned, rule-based predictive policy designed for decentralized, partially-observed, and time-varying environments. We compare this decentralized, information-driven policy to the classical, centralized Markov Decision Process (MDP) hedging-point policy for M/M/2 systems. Numerical experiments quantify the tradeoffs in average delay, impatience and robustness to stale information. Results show that when full, instantaneous state information and stationarity hold, the hedging-point policy yields less impatience but this diminishes as information becomes partial or stale. The rule-based predictive policy on the other hand is more robust to staleness in dispatched information, making it conducive for conditions typical of edge cloud and non-terrestrial deployments.