A Taxonomy of Schedulers -- Operating Systems, Clusters and Big Data Frameworks

Leszek Sliwko

doi:10.34257/GJCSTBVOL19IS1PG25

A Taxonomy of Schedulers -- Operating Systems, Clusters and Big Data Frameworks

Distributed, Parallel, and Cluster Computing 2025-11-05 v1

Authors: Leszek Sliwko

View on arXiv ↗ PDF ↗ DOI ↗

Abstract

This review analyzes deployed and actively used workload schedulers' solutions and presents a taxonomy in which those systems are divided into several hierarchical groups based on their architecture and design. While other taxonomies do exist, this review has focused on the key design factors that affect the throughput and scalability of a given solution, as well as the incremental improvements which bettered such an architecture. This review gives special attention to Google's Borg, which is one of the most advanced and published systems of this kind.

Cite

@article{arxiv.2511.01860,
  title  = {A Taxonomy of Schedulers -- Operating Systems, Clusters and Big Data Frameworks},
  author = {Leszek Sliwko},
  journal= {arXiv preprint arXiv:2511.01860},
  year   = {2025}
}

Comments

This is the accepted author's version of the paper. The final published version is available in Global Journal of Computer Science and Technology, 2019

A Taxonomy of Schedulers -- Operating Systems, Clusters and Big Data Frameworks

Abstract

Cite

Comments

Related papers