Classification and Evaluation of the Algorithms for Vector Bin Packing

Heuristics for Vector Bin Packing (VBP) play an important role in modern distributed computing systems and other applications aimed at optimizing the usage of multidimensional resources. In this paper we perform a systematic classification of heuristics for VBP. We bring together existing VBP algorithms and their tuning parameters, and propose new algorithms and new tuning parameters. For the relatively lesser studied class of multi-bin algorithms, we explore their properties analytically, considering monotonic and anomalous behavior and approximation guarantees. For empirical evaluation, all algorithms are implemented as the Vectorpack library and assessed through extensive experiments. Our findings may serve as the basis for the development of more complex, hybrid algorithms, hyperheuristics and machine learning algorithms. The Vectorpack library can be also adjusted for addressing enhanced VBP problems with additional features, which arise in applications, especially those typical for modern distributed computing systems