The VCG Mechanism for Bayesian Scheduling

TL;DR

This paper analyzes the VCG mechanism for scheduling tasks on selfish machines with random execution times, showing it achieves a near-optimal approximation ratio under certain distribution assumptions, improving previous bounds.

Contribution

It proves the VCG mechanism attains an $O(rac{ ext{ln} n}{ ext{ln} ext{ln} n})$ approximation ratio for Bayesian scheduling, improving prior bounds and identifying conditions for constant approximation.

Findings

VCG mechanism achieves $O(rac{ ext{ln} n}{ ext{ln} ext{ln} n})$ approximation ratio.

Improved bounds under Monotone Hazard Rate (MHR) distributions.

Conditions identified for constant approximation ratios regardless of task number.

Abstract

We study the problem of scheduling $m$ tasks to $n$ selfish, unrelated machines in order to minimize the makespan, where the execution times are independent random variables, identical across machines. We show that the VCG mechanism, which myopically allocates each task to its best machine, achieves an approximation ratio of $O\left(\frac{\ln n}{\ln \ln n}\right)$. This improves significantly on the previously best known bound of $O\left(\frac{m}{n}\right)$ for prior-independent mechanisms, given by Chawla et al. [STOC'13] under the additional assumption of Monotone Hazard Rate (MHR) distributions. Although we demonstrate that this is in general tight, if we do maintain the MHR assumption, then we get improved, (small) constant bounds for $m\geq n\ln n$ i.i.d. tasks, while we also identify a sufficient condition on the distribution that yields a constant approximation ratio regardless of the number of tasks.