Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.01860 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917057738571776 |
|---|---|
| author | Sliwko, Leszek |
| author_facet | Sliwko, Leszek |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01860 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Taxonomy of Schedulers -- Operating Systems, Clusters and Big Data Frameworks Sliwko, Leszek Distributed, Parallel, and Cluster Computing 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. |
| title | A Taxonomy of Schedulers -- Operating Systems, Clusters and Big Data Frameworks |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2511.01860 |