Adaptive Decentralized Queue Disclosure for Impatient Tenants in Edge and Non-terrestrial Systems

TL;DR

This paper introduces a decentralized, adaptive queue management strategy for edge systems that balances delay and robustness, outperforming traditional policies under partial and stale information conditions.

Contribution

It proposes a novel information-bulletin strategy with a rule-based predictive policy for decentralized queue control in dynamic, partially observed environments.

Findings

The predictive policy is more robust to stale information than classical methods.

Full state information yields better performance with traditional policies.

The approach is suitable for edge and non-terrestrial systems with limited information accuracy.

Abstract

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.