Exploiting and Mitigating Staleness in Distributed Edge Offloading via Predictive Load Awareness
Sawsan Ali Hamid, Yassine Boujelben, Faouzi Zarai · Network · 2026
Distributed offloading systems rely on periodic broadcasts to disseminate server state, yet propagation delays inevitably leave agents operating with stale information at decision time. Staleness is typically viewed as a performance limitation that should be minimized. This work revisits this assumption by studying its role in a distributed asynchronous offloading framework that operates under delayed and potentially stale load information. Players make independent server-selection decisions using locally available information and may optionally compensate for staleness through lightweight load-prediction mechanisms. A central finding is that staleness can act as an implicit coordination mechanism. By inducing heterogeneous and desynchronized perceptions of system state, it naturally diversifies agent decisions and mitigates collective migration oscillations that arise under perfect information sharing. These oscillations are shown to significantly delay convergence and can lead to unstable behavior in lightly loaded regimes. In contrast, stale yet diverse views prevent synchronized reactions and promote faster stabilization. The results further show that the value of prediction increases with communication staleness. As broadcast intervals grow and server-state information becomes increasingly outdated, prediction-based approaches substantially reduce performance degradation, improve load-balancing fairness, and lower migration activity relative to stale-information decisions. The numerical results show that increasing the broadcast interval from 0.5 s to 4 s causes a performance deterioration of approximately 95% for stale-information decisions, whereas prediction-based approaches limit this degradation to less than 15% over the same range. These findings suggest that the objective of distributed offloading should not be to eliminate staleness entirely, but rather to combine its stabilizing effects with lightweight predictive mechanisms that mitigate its negative impact on decision quality.