Discovering valuations and enforcing truthfulness in a deadline-aware scheduler

Abstract

A cloud computing cluster equipped with a deadline-aware job scheduler faces fairness and efficiency challenges when greedy users falsely advertise the urgency of their jobs. Penalizing such untruthfulness without demotivating users from using the cloud service calls for advanced mechanism design techniques that work together with deadline-aware job scheduling. We propose a Bayesian incentive compatible pricing mechanism based on matching by replica-surrogate valuation functions. User valuations can be discovered by the mechanism, even when the users themselves do not fully understand their own valuations. Furthermore, users who are charged a Bayesian incentive compatible price have no reason to lie about the urgency of their jobs. The proposed mechanism achieves multiple desired truthful properties such as Bayesian incentive compatibility and ex-post individual rationality. We implement the proposed pricing mechanism. Through experiments in a Hadoop cluster with real-world datasets, we show that our prototype is capable of suppressing untruthful behavior from users.