Improved Approximations for Multiprocessor Scheduling Under Uncertainty

This paper presents improved approximation algorithms for the problem of multiprocessor scheduling under uncertainty (SUU), in which the execution of each job may fail probabilistically. This problem is motivated by the increasing use of distributed computing to handle large, computationally intensive tasks. In the SUU problem we are given n unit-length jobs and m machines, a directed acyclic graph G of precedence constraints among jobs, and unrelated failure probabilities qij for each job j when executed on machine i for a single timestep. Our goal is to find a schedule that minimizes the expected makespan. Lin and Rajaraman gave the first approximations for this NPhard problem for the special cases of independent jobs, precedence constraints forming disjoint chains, and precedence constraints forming trees. In this paper, we present asymptotically better approximation algorithms. In particular, we improve upon the previously best O(log n)-approximation, giving an O(log log(min {m, n...

• Type: Journal
• Year: 2008
• Where: CORR
• Authors: Christopher Y. Crutchfield, Zoran Dzunic, Jeremy T. Fineman, David R. Karger, Jacob Scott
• Keywords: Approximation Algorithms, CORR 2008, Education, Independent Jobs, Precedence Constraints