Work

Non-preemptive Throughput Maximization under Time-varying Capacity

Year: 2025

Type: conference-paper

Abstract: We study the problem of scheduling jobs on a machine with time-varying capacity. The capacity at each time slot represents the maximum number of jobs that can be scheduled in parallel at that time slot. Each job is associated with a release time, processing time, deadline, and a profit. The objective is to find a non-preemptive schedule that respects all capacity constraints and maximizes the throughput, defined as the total profit of jobs that complete within their deadline. We consider different variants of the problem depending on job profits (identical / arbitrary), capacities (small / large) and environment (offline / online).

Source: ACM Symposium on Parallelism in Algorithms and Architectures (SPAA)

DOI: 10.1145/3694906.3743338

Authors: Aniket Murhekar, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua Wang

Institutions: University of Illinois Urbana-Champaign, Google (United States)

Language: English

Topic: Scheduling and Optimization Algorithms

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4412480925

FWCI:

Cites: 26

Cited by:

Related to: 10