TY - JOUR
AU1 - Giannakopoulos, Yiannis
AU2 - Kyropoulou, Maria
AB - We study the problem of scheduling m tasks to n selfish, unrelated machines in order to minimize the makespan, in which 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(ln n ln / ln n). This improves significantly on the previously best known bound of O(m/n) for prior-independent mechanisms, given by Chawla et al. [7] under the additional assumption of Monotone Hazard Rate (MHR) distributions. Although we demonstrate that this is tight in general, if we do maintain the MHR assumption, then we get improved, (small) constant bounds for m ≥ n ln n i.i.d. tasks. We also identify a sufficient condition on the distribution that yields a constant approximation ratio regardless of the number of tasks.
TI - The VCG Mechanism for Bayesian Scheduling
JF - ACM Transactions on Economics and Computation (TEAC)
DO - 10.1145/3105968
DA - 2017-12-14
UR - https://www.deepdyve.com/lp/association-for-computing-machinery/the-vcg-mechanism-for-bayesian-scheduling-SeWQY47PBp
SP - 1
EP - 16
VL - 5
IS - 4
DP - DeepDyve
ER -