Green Bin Packing

TL;DR

This paper introduces the green bin packing problem, an online variant that considers sustainability costs in server allocation, and proposes algorithms with theoretical and empirical performance improvements.

Contribution

It defines the green bin packing problem with cost considerations and develops new algorithms that outperform classical methods under certain cost conditions.

Findings

Classical algorithms perform well when eta G  1.

New algorithms improve performance when eta G  1.

Theoretical bounds and empirical results demonstrate effectiveness.

Abstract

The online bin packing problem and its variants are regularly used to model server allocation problems. Modern concerns surrounding sustainability and overcommitment in cloud computing motivate bin packing models that capture costs associated with highly utilized servers. In this work, we introduce the green bin packing problem, an online variant with a linear cost $\beta$ for filling above a fixed level $G$. For a given instance, the goal is to minimize the sum of the number of opened bins and the linear cost. We show that when $\beta G \le 1$, classical online bin packing algorithms such as FirstFit or Harmonic perform well, and can achieve competitive ratios lower than in the classic setting. However, when $\beta G > 1$, new algorithmic solutions can improve both worst-case and typical performance. We introduce variants of classic online bin packing algorithms and establish theoretical bounds, as well as test their empirical performance.