Parallel Online Algorithms for the Bin Packing Problem

TL;DR

This paper introduces parallel online algorithms for bin packing that improve competitive ratios by leveraging multiple processes, achieving near-optimal performance with fewer advice bits than previous methods.

Contribution

The paper presents Predictive Harmonic3, a family of k-copy algorithms for online bin packing with improved competitive ratios and reduced advice complexity.

Findings

PH3 achieves a competitive factor approaching 1.5 as k increases.

6 copies of PH3 outperform the best known single-copy algorithm.

4 bits of advice suffice to surpass previous lower bounds.

Abstract

We study \emph{parallel} online algorithms: For some fixed integer $k$, a collective of $k$ parallel processes that perform online decisions on the same sequence of events forms a $k$-\emph{copy algorithm}. For any given time and input sequence, the overall performance is determined by the best of the $k$ individual total results. Problems of this type have been considered for online makespan minimization; they are also related to optimization with \emph{advice} on future events, i.e., a number of bits available in advance. We develop \textsc{Predictive Harmonic}$_3$ (PH3), a relatively simple family of $k$-copy algorithms for the online Bin Packing Problem, whose joint competitive factor converges to 1.5 for increasing $k$. In particular, we show that $k=6$ suffices to guarantee a factor of $1.5714$ for PH3, which is better than $1.57829$, the performance of the best known 1-copy algorithm \textsc{Advanced Harmonic}, while $k=11$ suffices to achieve a factor of $1.5406$, beating the known lower bound of $1.54278$ for a single online algorithm. In the context of online optimization with advice, our approach implies that 4 bits suffice to achieve a factor better than this bound of $1.54278$, which is considerably less than the previous bound of 15 bits.