Overview

CCE supports different types of resource scheduling and task scheduling, improving application performance and overall cluster resource utilization. This section describes the main functions of CPU resource scheduling, GPU/NPU heterogeneous resource scheduling, and Volcano scheduling.

CPU Scheduling

CCE provides CPU policies to allocate complete physical CPU cores to applications, improving application performance and reducing application scheduling latency.

Function	Description	Documentation
CPU policy	When many CPU-intensive pods are running on a node, workloads may be migrated to different CPU cores. Many workloads are not sensitive to this migration and work fine without any intervention. For CPU-sensitive applications, you can use the CPU policy provided by Kubernetes to allocate dedicated cores to applications, improving application performance and reducing application scheduling latency.	CPU Policy
Enhanced CPU policy	Based on the Kubernetes static core binding policy, the enhanced CPU policy (enhanced-static) supports burstable pods (whose CPU requests and limits must be positive integers) and allows them to preferentially use certain CPUs to ensure application stability.	Enhanced CPU Policy

GPU Scheduling

CCE schedules heterogeneous GPU resources in clusters and allows GPUs to be used in containers.

Function	Description	Documentation
Default GPU scheduling in Kubernetes	This function allows you to specify the number of GPUs that a pod requests. The value can be less than 1 so that multiple pods can share a GPU.	Default GPU Scheduling in Kubernetes
GPU virtualization	GPU virtualization dynamically divides the GPU memory and computing power. A single GPU can be virtualized into up to 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 Virtualization

NPU Scheduling

CCE schedules heterogeneous NPU resources in a cluster to quickly and efficiently perform inference and image recognition.

Function	Description	Documentation
NPU scheduling	NPU scheduling allows you to specify the number of NPUs that a pod requests to provide NPU resources for workloads.	NPU Scheduling

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
Resource utilization-based scheduling	Scheduling policies are optimized for computing resources to effectively reduce resource fragments on each node and maximize computing resource utilization.	Resource Usage-based Scheduling
Priority-based scheduling	Scheduling policies are customized based on service importance and priorities to guarantee the resources of key services.	Priority-based Scheduling
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.	AI Performance-based Scheduling
NUMA affinity scheduling	Volcano targets to lift the limitation to make scheduler NUMA topology aware so that: Pods are not scheduled to the nodes that NUMA topology does not match. Pods are scheduled to the most suitable node for NUMA topology.	NUMA Affinity Scheduling

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.	Dynamic Resource Oversubscription
CPU Burst	CPU Burst is an elastic traffic limiting mechanism that allows temporarily exceeding the CPU limit to reduce the long-tail response time of services and improve the quality of latency-sensitive services.	CPU Burst
Egress network bandwidth guarantee	The egress network bandwidth used by online and offline services is balanced to ensure sufficient network bandwidth for online services.	Guaranteed Egress Network Bandwidth