Work

Online Load and Graph Balancing for Random Order Inputs

Year: 2024

Type: conference-paper

Abstract: Online load balancing for heterogeneous machines aims to minimize the makespan (maximum machine workload) by scheduling arriving jobs with varying sizes on different machines. In the adversarial setting, where an adversary chooses not only the collection of job sizes but also their arrival order, the problem is well-understood and the optimal competitive ratio is known to be Θ(log m) where m is the number of machines. In the more realistic random arrival order model, the understanding is limited. Previously, the best lower bound on the competitive ratio was only Ω(log log m).

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

DOI: 10.1145/3626183.3659983

Authors: Sungjin Im, Ravi Kumar, Shi Li, Aditya Petety, Manish Purohit

Institutions: University of California, Merced, Google (United States), Nanjing University

Language: English

Topic: Optimization and Search Problems

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Universitas Brawijaya, Office of Naval Research

Awards: G2887513492, Collaborative Research: AF: Small: Foundations of Algorithms Augmented with Predictions, G4720003262, CAREER: New Algorithmic Foundations for Online Scheduling, G8876996369