Parameter learning online algorithm for multiprocessor scheduling with rejection

Abstract

In multiprocessor scheduling with rejection the jobs are characterized by a processing time and a penalty and it is possible to reject the jobs. The goal is to minimize the makespan of the schedule for the accepted jobs plus the sum of the penalties of the rejected jobs. In this paper we present a new online algorithm for the problem. Our algorithm is a parameter learning extension of the total reject penalty algorithm. The efficiency of the algorithm is investigated by an experimental analysis.