Improved algorithms for online load balancing

TL;DR

This paper introduces new algorithms for online load balancing that achieve optimal regret bounds for general norms, including polynomial-time solutions for the challenging $L_ abla$-norm case, advancing the efficiency and effectiveness of online task allocation.

Contribution

The paper presents the first polynomial-time algorithms with optimal regret bounds for online load balancing across general norms, including the $L_ abla$-norm.

Findings

Achieves regret bounds matching the best known for $L_ abla$-norm.

Provides polynomial-time algorithms involving linear and second-order programming.

Extends online load balancing algorithms to general norm settings.

Abstract

We consider an online load balancing problem and its extensions in the framework of repeated games. On each round, the player chooses a distribution (task allocation) over $K$ servers, and then the environment reveals the load of each server, which determines the computation time of each server for processing the task assigned. After all rounds, the cost of the player is measured by some norm of the cumulative computation-time vector. The cost is the makespan if the norm is $L_\infty$-norm. The goal is to minimize the regret, i.e., minimizing the player's cost relative to the cost of the best fixed distribution in hindsight. We propose algorithms for general norms and prove their regret bounds. In particular, for $L_\infty$-norm, our regret bound matches the best known bound and the proposed algorithm runs in polynomial time per trial involving linear programming and second order programming, whereas no polynomial time algorithm was previously known to achieve the bound.