Improved algorithms for resource allocation under varying capacity

Abstract

We consider the problem of scheduling a set of jobs on a system that offers certain resource, wherein the amount of resource offered varies over time. For each job, the input specifies a set of possible scheduling instances, where each instance is given by starting time, ending time, profit and resource requirement. A feasible solution selects a subset of job instances such that at any timeslot, the total requirement by the chosen instances does not exceed the resource available at that timeslot, and at most one instance is chosen for each job. The above problem falls under the well-studied framework of unsplittable flow problem (UFP) on line. The generalized notion of scheduling possibilities captures the standard setting concerned with release times and deadlines. We present improved algorithms based on the primal-dual paradigm, where the improvements are in terms of approximation ratio, running time and simplicity. © 2014 Springer-Verlag Berlin Heidelberg.