Scheduling Overview¶
CCE supports multiple resource and task scheduling policies to enhance application performance and overall cluster resource utilization. This section describes the main functions of CPU scheduling, GPU heterogeneous scheduling, and Volcano scheduling.
CPU Scheduling¶
CCE provides CPU management policies that enable the allocation of complete physical CPU cores to applications. This improves application performance and reduces scheduling latency.
Function | Description | Documentation |
|---|---|---|
CPU policy | If a node runs a large number of CPU-intensive pods, workloads may be migrated between CPU cores. For CPU-sensitive applications, you can allocate dedicated physical cores to them using the CPU management policy provided by Kubernetes. This improves application performance and reduces scheduling latency. | |
Enhanced CPU policy | Based on the conventional CPU management policy, this policy supports intelligent scheduling for burstable pods, whose CPU request and limit values must be positive integers. These pods can use specific CPU cores preferentially, but they do not exclusively use these CPU cores. |
GPU Scheduling¶
CCE provides GPU scheduling for clusters, facilitating refined resource allocation and optimizing resource utilization. This accommodates the specific GPU compute needs of diverse workloads, thereby enhancing the overall scheduling efficiency and service performance of the cluster.
Function | Description | Documentation |
|---|---|---|
Default GPU scheduling in Kubernetes | You can specify the number of GPUs that a pod requests. The value can be less than 1 so that multiple pods can share a single GPU. | |
GPU virtualization | GPU virtualization dynamically divides the GPU memory and computing power. A single GPU can be virtualized into a maximum of 20 virtual GPU devices. Virtualization is more flexible than static allocation. You can specify the number of GPUs on the basis of stable service running to improve GPU utilization. | |
GPU monitoring | Prometheus and Grafana comprehensively monitor GPU metrics. This helps optimize compute performance, quickly identify faults, and efficiently schedule resources. This leads to improved GPU utilization and reduced O&M costs. | |
GPU auto scaling | CCE allows you to configure auto scaling policies for workloads and nodes based on GPU metrics to dynamically schedule and optimize resources. This improves computing efficiency, ensures stable service operation, and reduces O&M costs. |
Volcano Scheduling¶
Volcano is a Kubernetes-based batch processing platform that supports machine learning, deep learning, bioinformatics, genomics, and other big data applications. It provides general-purpose, high-performance computing capabilities, such as job scheduling, heterogeneous chip management, and job running management.
Function | Description | Documentation |
|---|---|---|
Scheduling workloads | Kubernetes typically uses its default scheduler to schedule workloads. To use Volcano, specify Volcano for your workloads. | |
Resource utilization-based scheduling | Scheduling policies are optimized for computing resources to effectively reduce resource fragments on each node and maximize computing resource utilization. | |
Priority-based scheduling | Scheduling policies are customized based on service importance and priorities to guarantee the resources of key services. | |
AI performance-based scheduling | Scheduling policies are configured based on the nature and resource usage of AI tasks to increase the throughput of cluster services and improve service performance. | |
NUMA affinity scheduling | Volcano targets to lift the limitation to make scheduler NUMA topology aware so that:
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Cloud Native Hybrid Deployment¶
The cloud native hybrid deployment solution focuses on the Volcano and Kubernetes ecosystems to help users improve resource utilization and efficiency and reduce costs.
Function | Description | Documentation |
|---|---|---|
Dynamic resource oversubscription | Based on the types of online and offline jobs, Volcano scheduling is used to utilize the resources that are requested but not used in the cluster (the difference between the number of requested resources and the number of used resources) for resource oversubscription and hybrid deployment to improve cluster resource utilization. |