When Backpressure Meets Predictive Scheduling

Motivated by the increasing popularity of learning and predicting human user behavior in communication and computing systems, in this paper, we investigate the fundamental benefit of predictive scheduling, i.e., predicting and pre-serving arrivals, in controlled queueing systems. Based on a lookahead-window prediction model, we first establish a novel queue-equivalence between the predictive queueing system with a fully efficient scheduling scheme and an equivalent queueing system without prediction. This result allows us to analytically demonstrate that predictive scheduling necessarily improves system delay performance and drives it to zero with increasing prediction power. It also enables us to exactly determine the required prediction power for different systems and study its impact on tail delay. We then propose the Predictive Backpressure (PBP) algorithm for achieving optimal utility performance in such predictive systems. PBP efficiently incorporates prediction into stochastic system control and avoids the great complication due to the exponential state space growth in the prediction window size. We show that PBP achieves a utility performance that is within O (ϵ) of the optimal, for any ϵ > 0, while guaranteeing that the system delay distribution is a shifted-to-the-left version of that under the original Backpressure algorithm. Hence, the average delay under PBP is strictly better than that under Backpressure, and vanishes with increasing prediction window size. This implies that the resulting utility-delay tradeoff with predictive scheduling can beat the known optimal [O (ϵ), O (log(1/ϵ))] tradeoff for systems without prediction. We also develop the Predictable-Only PBP (POPBP) algorithm and show that it effectively reduces packet delay in systems where traffic can only be predicted but not pre-served.