Details
| Original language | English |
|---|---|
| Article number | 3544788 |
| Journal | ACM computing surveys |
| Volume | 55 |
| Issue number | 7 |
| Early online date | 24 Jun 2022 |
| Publication status | Published - 15 Dec 2022 |
Abstract
Since its release in 2014, Kubernetes has become a popular choice for orchestrating containerized workloads at scale. To determine the most appropriate node to host a given workload, Kubernetes makes use of a scheduler that takes into account a set of hard and soft constraints defined by workload owners and cluster administrators. Despite being highly configurable, the default Kubernetes scheduler cannot fully meet the requirements of emerging applications, such as machine/deep learning workloads and edge computing applications. This has led to different proposals of custom Kubernetes schedulers that focus on addressing the requirements of the aforementioned applications. Since the related literature is growing in this area, we aimed, in this survey, to provide a classification of the related literature based on multiple criteria, including scheduling objectives as well as the types of considered workloads and environments. Additionally, we provide an overview of the main approaches that have been adopted to achieve each objective. Finally, we highlight a set of gaps that could be leveraged by academia or the industry to drive further research and development activities in the area of custom scheduling in Kubernetes.
Keywords
- Kubernetes, scheduling, survey, workload placement
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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In: ACM computing surveys, Vol. 55, No. 7, 3544788, 15.12.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Custom Scheduling in Kubernetes
T2 - A Survey on Common Problems and Solution Approaches
AU - Rejiba, Zeineb
AU - Chamanara, Javad
N1 - Funding Information: This work has received funding from the BRAINE Project (“Big data pRocessing and Artificial Intelligence at the Network Edge”), ECSEL Joint Undertaking (JU) under grant agreement No 876967. The JU receives support from the European Union’s Horizon 2020 research and innovation program and Italy, Poland, Denmark, Netherlands, Ireland, Hungary, Germany, France, Slovakia, Bulgaria, Finland, Czech Republic, Israel, Switzerland.
PY - 2022/12/15
Y1 - 2022/12/15
N2 - Since its release in 2014, Kubernetes has become a popular choice for orchestrating containerized workloads at scale. To determine the most appropriate node to host a given workload, Kubernetes makes use of a scheduler that takes into account a set of hard and soft constraints defined by workload owners and cluster administrators. Despite being highly configurable, the default Kubernetes scheduler cannot fully meet the requirements of emerging applications, such as machine/deep learning workloads and edge computing applications. This has led to different proposals of custom Kubernetes schedulers that focus on addressing the requirements of the aforementioned applications. Since the related literature is growing in this area, we aimed, in this survey, to provide a classification of the related literature based on multiple criteria, including scheduling objectives as well as the types of considered workloads and environments. Additionally, we provide an overview of the main approaches that have been adopted to achieve each objective. Finally, we highlight a set of gaps that could be leveraged by academia or the industry to drive further research and development activities in the area of custom scheduling in Kubernetes.
AB - Since its release in 2014, Kubernetes has become a popular choice for orchestrating containerized workloads at scale. To determine the most appropriate node to host a given workload, Kubernetes makes use of a scheduler that takes into account a set of hard and soft constraints defined by workload owners and cluster administrators. Despite being highly configurable, the default Kubernetes scheduler cannot fully meet the requirements of emerging applications, such as machine/deep learning workloads and edge computing applications. This has led to different proposals of custom Kubernetes schedulers that focus on addressing the requirements of the aforementioned applications. Since the related literature is growing in this area, we aimed, in this survey, to provide a classification of the related literature based on multiple criteria, including scheduling objectives as well as the types of considered workloads and environments. Additionally, we provide an overview of the main approaches that have been adopted to achieve each objective. Finally, we highlight a set of gaps that could be leveraged by academia or the industry to drive further research and development activities in the area of custom scheduling in Kubernetes.
KW - Kubernetes
KW - scheduling
KW - survey
KW - workload placement
UR - http://www.scopus.com/inward/record.url?scp=85146371474&partnerID=8YFLogxK
U2 - 10.1145/3544788
DO - 10.1145/3544788
M3 - Article
AN - SCOPUS:85146371474
VL - 55
JO - ACM computing surveys
JF - ACM computing surveys
SN - 0360-0300
IS - 7
M1 - 3544788
ER -