Scheduler for Distributed and Collaborative Container Clusters based on Multi-Resource Metric

Lee, Yena; An, Jae-Hoon; Kim, Young‐Hwan

doi:10.1145/3400286.3418281

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…Multi-cluster systems currently lack concrete co-scheduling mechanisms, and only recently have new synchronization mechanisms been proposed. These include the K8s Sigs co-scheduling plugin 37 (which is in beta 34 https://keda.sh 35 https://www.kubesphere.io 36 https://github.com/jpedro1992/network-topology-controller 37 https://github.com/kubernetes-sigs/schedulerplugins/tree/master/pkg/coscheduling status), Admiralty 38 , the k8s-spark-scheduler 39 (no longer maintained), and recent research efforts such as RLSK [44] and Twine [58].…”

Section: B Adaptive Scheduling and Workload Migrationmentioning

confidence: 99%

A Framework for Cognitive, Decentralized Container Orchestration

Sofia,

Salomon,

Ferlin-Reiter

et al. 2024

IEEE Access

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This perspective paper introduces a novel framework for container orchestration called CODECO. The CODECO orchestration framework relies on a data-network-computing approach to define the best infrastructure that can support the operation of next-generation Internet applications across a mobile, heterogeneous Edge-Cloud continuum. The selection of such an infrastructure is aligned with target performance profiles defined by the user, such as resilience or greenness. CODECO proposes to rely on decentralized Artificial Intelligence approaches in an attempt to provide the most suitable infrastructure to an application deployment, considering infrastructural challenges, such as intermittent connectivity and node failure. This paper explains the current CODECO framework and provides insight into operational use-cases where CODECO is being deployed, as relevant examples of application for such a framework. Recent developments in the creation of the open-source CODECO framework are described and explained, allowing the use of the framework by the research community. The paper then provides a thorough analysis of CODECO's features in comparison with existing orchestration frameworks, explaining the benefits introduced with this dynamic orchestration approach.INDEX TERMS Edge-Cloud; Orchestration; Kubernetes; AI/ML; heterogeneous networks; data observability.

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Section: B Adaptive Scheduling and Workload Migrationmentioning

confidence: 99%

A Framework for Cognitive, Decentralized Container Orchestration

Sofia,

Salomon,

Ferlin-Reiter

et al. 2024

IEEE Access

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“…The simulation results indicate that RLSK can achieve improvements in load balancing and resource utilization, at the cost of a slight increase in the makespan. The authors in [68] propose a scheduler for federated Kubernetes clusters with the goal to facilitate the mobility of services between diferent clusters and expanding upon the resources of a single cluster. The proposed scheduler builds upon the 2-stage scheduling process in Kubernetes by applying iltering and scoring on the set of clusters taking part in the federation.…”

Section: Specific Contributionsmentioning

confidence: 99%

“…Discussion: As it can be noted from the previous works, the proposals made to support scheduling across multiple clusters inherit the concepts of iltering and scoring from the default, single-cluster Kubernetes scheduler. However, in most of these works [47,50,68], iltering and scoring are performed at the cluster level, instead of the node level. In addition, these works envision the iltering and the scoring steps to be made at a global level, where the scheduler has a full view of the individual clusters' information.…”

Section: Specific Contributionsmentioning

confidence: 99%

Custom Scheduling in Kubernetes: A Survey on Common Problems and Solution Approaches

Rejiba

Chamanara

2022

ACM Comput. Surv.

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Since its release in 2014, Kubernetes has become a popular choice for orchestrating containerized workloads at scale. In order 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 the academia or industry to drive further research and development activities in the area of custom scheduling in Kubernetes.

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