QoS-Aware Choreography in the Internet of Things Based on Forecast-Guided Binding and Local Rebinding
TL;DR
This paper proposes MDP-BWO, a Markov decision process (MDP)-guided Black Widow Optimization (BWO) framework that integrates state forecasting, dependency-aware binding and runtime adaptation and is matched or slightly exceeded only by an exact mixed-integer search with 14–43 times longer mean planning time.
Abstract
Internet of Things (IoT) service choreographies integrate services across devices, edge servers and the cloud without a central orchestrator. Existing quality-of-service (QoS)-aware composition methods use single-value QoS, separable objectives and one-shot binding, overlooking evolving service conditions, peer-link dependencies and rebinding costs. This paper proposes MDP-BWO, a Markov decision process (MDP)-guided Black Widow Optimization (BWO) framework that integrates state forecasting, dependency-aware binding and runtime adaptation. A three-state Markov chain learned from monitoring traces forecasts each service’s state occupancy and guides BWO over constrained, non-separable bindings; each participant rebinds locally by table lookup of a finite-horizon MDP policy over a value-selected shortlist. In simulations on synthetic and WS-DREAM-based instances (measured QoS, synthetic state dynamics; 14 configurations × 30 held-out seeds), MDP-BWO outperforms six baselines with reactive fail-over in every configuration, raising executed utility by 0.086–0.139 and the solved-request rate by 0.087–0.130 (Holm-corrected p < 0.05); latency on WS-DREAM and cost on synthetic instances do not improve over three of the population baselines. Most of the gain comes from MDP rebinding; with the same forecast and policy, MDP-BWO still outperforms five population-based searches in 11–14 configurations and is matched or slightly exceeded only by an exact mixed-integer search with 14–43 times longer mean planning time.