Auto scaling is a service that can dynamically scale computing resources to meet your business requirements. Auto scaling provides a more cost-effective method to manage your resources. This topic introduces auto scaling and the related components.
Background
Auto scaling is widely used in Container Service for Kubernetes (ACK) clusters. Typically,
auto scaling is used in scenarios such as online workload scaling, large-scale computing
and training, GPU-accelerated deep learning, inference and training based on shared
GPU resources, and periodical workload scheduling. Auto scaling enables elasticity
from the following aspects:
- Workload scaling. Auto scaling can adjust workloads, such as pods. For example, Horizontal Pod Autoscaler (HPA) is a typical workload scaling component that can change the number of replicated pods to scale the workload.
- Resource scaling. If the resources of a cluster cannot meet the scaling requirements of workloads, Elastic Compute Service (ECS) instances or elastic container instances are added to the cluster.
Scaling components for ACK clusters
Components for workload scaling| Component | Introduction | Scenario | Limit | References |
|---|---|---|---|---|
| HPA | A built-in component of Kubernetes. HPA is mainly used for online applications. | Online business | HPA uses Deployments and StatefulSets to scale workloads. | HPA |
| Vertical Pod Autoscaler (VPA) | An open source component. VPA is mainly used for monolithic applications. | Monolithic applications | VPA is applicable to applications that cannot be horizontally scaled. In practical scenarios, VPA is used when pods are recovered from anomalies. | Vertical pod autoscaling |
| CronHPA | An open source component provided by ACK. CronHPA is applicable to applications whose resource usage periodically changes. | Periodically changing workloads | CronHPA uses Deployments and StatefulSets to scale workloads. CronHPA is compatible with HPA. You can use CronHPA and HPA in combination to scale workloads. | CronHPA |
| Elastic-Workload | A component provided by ACK. Elastic-Workload is mainly used in scenarios where fine-grained scaling is required, for example, you want to distribute a workload to different zones. | Scenarios where fine-grained scaling is required | Elastic-Workload is applicable to online workloads that require fine-grained scaling. For example, some pods of a Deployment are scheduled to an ECS instance, and the rest of the pods are scheduled to elastic container instances. | Install the elastic workload component |
Components for resource scaling
| Component | Description | Scenario | Time cost for delivery | References |
|---|---|---|---|---|
| cluster-autoscaler | cluster-autoscaler is an open source component provided by Kubernetes that can scale nodes in a cluster. cluster-autoscaler is integrated with auto scaling to provide more elastic and cost-effective scaling services. | cluster-autoscaler is applicable to all scenarios, especially online workloads, deep learning, and large-scale computing. | The time required to add 1,00 nodes to a cluster:
|
Auto scaling of nodes |
| virtual-node | virtual-node is an open source component provided by ACK. virtual-node provides the runtime for serverless applications. Developers do not need to handle node resources and only need to create, manage, and pay for pods based on the actual usage. | virtual-node is used to handle traffic spikes, continuous integration and continuous delivery (CD/CD), and big data computing. | The time required to create 1,000 pods in a cluster:
|
Deploy the virtual node controller and use it to create Elastic Container Instance-based pods |
| virtual-kubelet-autoscaler | virtual-kubelet-autoscaler is a component provided by ACK. virtual-kubelet-autoscaler is used to scale serverless applications. | virtual-kubelet-autoscaler is used to handle traffic spikes, CI/CD, and big data computing. | The time required to create 1,000 pods in a cluster:
|
Install virtual-kubelet-autoscaler |