TY - JOUR
T1 - Network SLO-aware container scheduling in Kubernetes
AU - Kim, Eunsook
AU - Lee, Kyungwoon
AU - Yoo, Chuck
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023/7
Y1 - 2023/7
N2 - In clouds, various services run on respective containers and have service-level objectives (SLO) that significantly impact service qualities. However, Kubernetes, a widely used container orchestration platform, does not schedule containers with respect to the network SLOs. This paper proposes a new container scheduling technique consisting of a cloud-level and node-level scheduler. The cloud-level scheduler selects a node that is best suited for satisfying the network SLO, and the node-level scheduler adjusts the CPU allocation for the container to satisfy SLOs on the selected node. We implement the cloud-level scheduler in Kubernetes and the node-level scheduler in the Linux kernel module and evaluate them using simulation and actual deployment. The evaluation results show that the cloud-level scheduler reduces the scheduling overhead by 22× compared to DRF, a representative multi-resource scheduling technique. Also, the node-level scheduler increases the number of containers that satisfy SLOs by 2.5× compared to native Kubernetes, which will significantly enhance the service quality of user-facing services.
AB - In clouds, various services run on respective containers and have service-level objectives (SLO) that significantly impact service qualities. However, Kubernetes, a widely used container orchestration platform, does not schedule containers with respect to the network SLOs. This paper proposes a new container scheduling technique consisting of a cloud-level and node-level scheduler. The cloud-level scheduler selects a node that is best suited for satisfying the network SLO, and the node-level scheduler adjusts the CPU allocation for the container to satisfy SLOs on the selected node. We implement the cloud-level scheduler in Kubernetes and the node-level scheduler in the Linux kernel module and evaluate them using simulation and actual deployment. The evaluation results show that the cloud-level scheduler reduces the scheduling overhead by 22× compared to DRF, a representative multi-resource scheduling technique. Also, the node-level scheduler increases the number of containers that satisfy SLOs by 2.5× compared to native Kubernetes, which will significantly enhance the service quality of user-facing services.
KW - Container scheduling
KW - Network performance
KW - Service quality
KW - Service-level objectives
UR - http://www.scopus.com/inward/record.url?scp=85149015780&partnerID=8YFLogxK
U2 - 10.1007/s11227-023-05122-5
DO - 10.1007/s11227-023-05122-5
M3 - Article
AN - SCOPUS:85149015780
SN - 0920-8542
VL - 79
SP - 11478
EP - 11494
JO - Journal of Supercomputing
JF - Journal of Supercomputing
IS - 10
ER -