Improved algorithms for resource allocation under varying capacity

Venkatesan T. Chakaravarthy (), Anamitra R. Choudhury (), Shalmoli Gupta (), Sambudha Roy () and Yogish Sabharwal ()
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Venkatesan T. Chakaravarthy: IBM Research
Anamitra R. Choudhury: IBM Research
Shalmoli Gupta: University of Illinois at Urbana-Champaign
Sambudha Roy: Linkedin
Yogish Sabharwal: IBM Research

Journal of Scheduling, 2018, vol. 21, issue 3, No 4, 313-325

Abstract: 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 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.

Keywords: Unsplittable flow problem; Approximation algorithm (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10951-017-0515-3

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