Approximation algorithms for multiprocessor scheduling under uncertainty

• Authors: Guolong Lin;Rajmohan Rajaraman
• Venue: Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
• Year: 2007

Motivated by applications in grid computing and project management, we study multiprocessor scheduling in scenarios where there is uncertainty in the successful execution of jobs when assigned to processors. We consider the problem of multiprocessor scheduling under uncertainty, in which we are given n unit-time jobs and m machines, a directed acyclic graph C giving the dependencies among the jobs, and for every job j and machine i, the probability pij of the successful completion of job j when scheduled on machine i in any given particular step. The goal of the problem is to find a schedule that minimizes the expected makespan, that is, the expected completion time of all the jobs. The problem of multiprocessor scheduling under uncertainty was introduced by Malewicz and was shown to be NP-hard even when all the jobs are independent. In this paper, we present polynomial-time approximation algorithms for the problem, for special cases of the dag C. We obtain an O(log n)-approximation for the case of independent jobs, an O(log m log n log(n + m)/ log log(n + m))-approximation when C is a collection of disjoint chains, an O(log m log2 n)-approximation when C is a collection of directed out- or in-trees, and an O(log m log2 n log(n + m)/ log log(n + m))-approximation when C is a directed forest.