Online Load and Graph Balancing for Random Order Inputs

TL;DR

This paper advances understanding of online load balancing in the random order model by establishing a new lower bound on the competitive ratio and proposing an improved algorithm.

Contribution

It significantly improves the lower bound on the competitive ratio to () () and introduces an algorithm with better competitive performance.

Findings

Lower bound on competitive ratio improved to ()

Proposed algorithm achieves (/ ())-competitive ratio

Results apply even for restricted job size cases

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 $\Theta(\log m)$ 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 $\Omega(\log \log m)$. We significantly improve this bound by showing an $\Omega( \sqrt {\log m})$ lower bound, even for the restricted case where each job has a unit size on two machines and infinite size on the others. On the positive side, we propose an $O(\log m/\log \log m)$-competitive algorithm, demonstrating that better performance is possible in the random arrival model.