Publikation
Adaptive Decentralized Queue Disclosure for Impatient Tenants in Edge and Non-terrestrial Systems
Anthony Kiggundu; Bin Han; Hans Dieter Schotten
In: Doctoral Symposium. IEEE Conference on Network Function Virtualization and Software Defined Networks (IEEE NFV-SDN-2025), Doctoral Symposium, located at NFV-SDN 2025, November 10-12, Athens, Greece, IEEEXplore, 12/2025.
Zusammenfassung
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 re-
nege 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, central-
ized 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.