Distributedness based scheduling
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909634890039296 |
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| author | Ranjan, Paritosh Majumder, Surajit Roy, Prodip Padhan, Bhuban |
| author_facet | Ranjan, Paritosh Majumder, Surajit Roy, Prodip Padhan, Bhuban |
| contents | Efficient utilization of computing resources in a Kubernetes cluster is often constrained by the uneven distribution of pods with similar usage patterns. This paper presents a novel scheduling strategy designed to optimize the distributedness of Kubernetes resources based on their usage magnitude and patterns across CPU, memory, network, and storage. By categorizing resource usage into labels such as "cpu high spike" or "memory medium always," and applying these to deployed pods, the system calculates the variance or distributedness factor of similar resource types across cluster nodes. A lower variance indicates a more balanced distribution. The Kubernetes scheduler is enhanced to consider this factor during scheduling decisions, placing new pods on nodes that minimize resource clustering. Furthermore, the approach supports redistribution of existing pods through simulated scheduling to improve balance. This method is adaptable at the cluster, namespace, or application level and is integrated within the standard Kubernetes scheduler, providing a scalable, label-driven mechanism to improve overall resource efficiency in cloud-native environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02581 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributedness based scheduling Ranjan, Paritosh Majumder, Surajit Roy, Prodip Padhan, Bhuban Distributed, Parallel, and Cluster Computing Efficient utilization of computing resources in a Kubernetes cluster is often constrained by the uneven distribution of pods with similar usage patterns. This paper presents a novel scheduling strategy designed to optimize the distributedness of Kubernetes resources based on their usage magnitude and patterns across CPU, memory, network, and storage. By categorizing resource usage into labels such as "cpu high spike" or "memory medium always," and applying these to deployed pods, the system calculates the variance or distributedness factor of similar resource types across cluster nodes. A lower variance indicates a more balanced distribution. The Kubernetes scheduler is enhanced to consider this factor during scheduling decisions, placing new pods on nodes that minimize resource clustering. Furthermore, the approach supports redistribution of existing pods through simulated scheduling to improve balance. This method is adaptable at the cluster, namespace, or application level and is integrated within the standard Kubernetes scheduler, providing a scalable, label-driven mechanism to improve overall resource efficiency in cloud-native environments. |
| title | Distributedness based scheduling |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2506.02581 |