We consider the problem of preemptively scheduling a set of n jobs on m (identical, uniformly related, or unrelated) parallel machines. The scheduler may reject a subset of the jobs and thereby incur jobdependent penalties for each rejected job, and he must construct a schedule for the remaining jobs so as to optimize the preemptive makespan on the m machines plus the sum of the penalties of th...

Clusters are now considered as an alternative to parallel machines to execute workloads made up of sequential and/or parallel applications. For efficient application execution on clusters, dynamic global process scheduling is of prime importance. Different dynamic scheduling policies that have been studied for distributed systems or parallel machines may be used in clusters. The choice of a par...

This paper reviews results related to the parallel machine scheduling problem under availability constraints. The motivation of this paper comes from the fact that no survey focusing on this specific problem was published. The problems of single machine, identical, uniform, and unrelated parallel machines under different constraints and optimizing various objective functions were analyzed. For ...

The parallel mechine scheduling problem with unrelated machines is studied where the objective is to minimize the maximum makespan. In this paper, new local search algorithms are proposed where the neighborhood search of a solution uses the “efficiency” of the machinea for each job. It is shown that this method yields better solutions and shorter running times than the more general local search...

In this paper, we introduce an experimental software tool called CASCH (Computer Aided SCHeduling) for automatic parallelization and scheduling of applications to parallel processors. CASCH transforms a sequential program to a parallel program through automatic task graph generation, scheduling, mapping, communication, and synchronization primitives insertion. The major strength of CASCH is its...

In this paper we present an approach for performing very large state-space search on parallel machines. While the majority of searching methods in ArtificiM Intelligence rely on heuristics, the paralld algorithm we propose exploits the algebraic structure of problems to reduce both the time and space complexity required to solve these problems on massively parallel machines. Our algorithms have...

In this paper, we provide a new class of randomized approximation algorithms for scheduling problems by directly interpreting solutions to so-called time-indexed LPs as probabilities. The most general model we consider is scheduling unrelated parallel machines with release dates (or even network scheduling) so as to minimize the average weighted completion time. The crucial idea for these multi...

We consider the problem of scheduling n jobs on m identical parallel machines. An optimal schedule to the proposed problem is defined as one that gives the smallest makespan (the completion time of the last job on any one of the parallel machines) among the set of all schedules with optimal total flowtime (the sum of the completion times of all jobs). We propose two new simple heuristic algorit...

Total weighted tardiness is a measure of customer satisfaction. Minimizing it represents satisfying the general requirement of on-time delivery. In this research, we consider an ant colony optimization (ACO) algorithm to solve the problem of scheduling unrelated parallel machines to minimize total weighted tardiness. The problem is NP-hard in the strong sense. Computational results show that th...

We consider the preemptive and non-preemptive problems of scheduling jobs with precedence constraints on parallel machines with the objective to minimize the sum of (weighted) completion times. We investigate an online model in which the scheduler learns about a job when all its predecessors have completed. For scheduling on a single machine, we show matching lower and upper bounds of Θ(n) and ...