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- Linux Kernel Integer Overflow Vulnerability (CVE-2022-0185)
- Linux Polkit Privilege Escalation Vulnerability (CVE-2021-4034)
- Notice on the Vulnerability of Kubernetes subPath Symlink Exchange (CVE-2021-25741)
- Notice of runC Vulnerability That Allows a Container Filesystem Breakout via Directory Traversal (CVE-2021-30465)
- Notice on the Docker Resource Management Vulnerability (CVE-2021-21285)
- Notice of NVIDIA GPU Driver Vulnerability (CVE-2021-1056)
- Notice on the Sudo Buffer Vulnerability (CVE-2021-3156)
- Notice on the Kubernetes Security Vulnerability (CVE-2020-8554)
- Notice of Apache containerd Security Vulnerability (CVE-2020-15257)
- Notice on the Docker Engine Input Verification Vulnerability (CVE-2020-13401)
- Notice of Kubernetes kube-apiserver Input Verification Vulnerability (CVE-2020-8559)
- Notice on the Kubernetes kubelet Resource Management Vulnerability (CVE-2020-8557)
- Notice on the Kubernetes kubelet and kube-proxy Authorization Vulnerability (CVE-2020-8558)
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- Notice on Fixing the Docker Command Injection Vulnerability (CVE-2019-5736)
- Notice on Fixing the Kubernetes Permission and Access Control Vulnerability (CVE-2018-1002105)
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Product Release Notes
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Cluster Versions
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Add-on Versions
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Cluster Versions
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Clusters
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Cluster Overview
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Kubernetes Version Release Notes
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- Buying a Cluster
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Managing a Cluster
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Upgrading a Cluster
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Troubleshooting for Pre-upgrade Check Exceptions
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Cluster Overview
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Nodes
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Management Nodes
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Node O&M
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Workloads
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Configuring a Workload
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Network
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Container Network
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Cloud Native Network 2.0 Settings
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Service
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LoadBalancer
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Ingresses
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LoadBalancer Ingresses
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Advanced Setting Examples of LoadBalancer Ingresses
- Configuring an HTTPS Certificate for a LoadBalancer Ingress
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Old Console
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Nodes
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Workloads
- Overview
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Configuring a Container
- Using a Third-Party Image
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Best Practices
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Security
- Configuration Suggestions on CCE Cluster Security
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API Reference
- Before You Start
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APIs
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Cluster Management
- Creating a Cluster
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Cluster Upgrade
- Upgrading a Cluster
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Chart Management
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- Kubernetes APIs
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Appendix
- Status Code
- Error Codes
- Obtaining a Project ID
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- Specifying Add-ons to Be Installed During Cluster Creation
- How to Obtain Parameters in the API URI
- Creating a VPC and Subnet
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- Node Flavor Description
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- Maximum Number of Pods That Can Be Created on a Node
- Node OS
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- SDK Reference
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FAQs
- Common FAQ
- Billing
- Cluster
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Node
- Node Creation
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Node Running
- What Should I Do If a Cluster Is Available But Some Nodes Are Unavailable?
- How Do I Log In to a Node Using a Password and Reset the Password?
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- What Should I Do If the vdb Disk of a Node Is Damaged and the Node Cannot Be Recovered After Reset?
- What Should I Do If I/O Suspension Occasionally Occurs When SCSI EVS Disks Are Used?
- How Do I Fix an Abnormal Container or Node Due to No Thin Pool Disk Space?
- How Do I Rectify Failures When the NVIDIA Driver Is Used to Start Containers on GPU Nodes?
- Specification Change
- OSs
- Node Pool
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Workload
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Workload Exception Troubleshooting
- How Can I Find the Fault for an Abnormal Workload?
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- What Should I Do If Container Startup Fails?
- What Should I Do If a Pod Fails to Be Evicted?
- What Should I Do If a Storage Volume Cannot Be Mounted or the Mounting Times Out?
- What Should I Do If a Workload Remains in the Creating State?
- What Should I Do If a Pod Remains in the Terminating State?
- What Should I Do If a Workload Is Stopped Caused by Pod Deletion?
- What Should I Do If an Error Occurs When I Deploy a Service on the GPU Node?
- How Can I Locate Faults Using an Exit Code?
- Container Configuration
- Scheduling Policies
-
Others
- What Should I Do If a Cron Job Cannot Be Restarted After Being Stopped for a Period of Time?
- What Is a Headless Service When I Create a StatefulSet?
- What Should I Do If Error Message "Auth is empty" Is Displayed When a Private Image Is Pulled?
- What Is the Image Pull Policy for Containers in a CCE Cluster?
- What Can I Do If a Layer Is Missing During Image Pull?
-
Workload Exception Troubleshooting
-
Networking
-
Network Exception Troubleshooting
- How Do I Locate a Workload Networking Fault?
- Why Does the Browser Return Error Code 404 When I Access a Deployed Application?
- What Should I Do If a Container Fails to Access the Internet?
- What Should I Do If a Node Fails to Connect to the Internet (Public Network)?
- What Should I Do If Nginx Ingress Access in the Cluster Is Abnormal After the NGINX Ingress Controller Add-on Is Upgraded?
- What Could Cause Access Exceptions After Configuring an HTTPS Certificate for a LoadBalancer Ingress?
- Network Planning
- Security Hardening
-
Network Configuration
- How Can Container IP Addresses Survive a Container Restart?
- How Can I Check Whether an ENI Is Used by a Cluster?
- How Can I Delete a Security Group Rule Associated with a Deleted Subnet?
- How Can I Synchronize Certificates When Multiple Ingresses in Different Namespaces Share a Listener?
- How Can I Determine Which Ingress the Listener Settings Have Been Applied To?
-
Network Exception Troubleshooting
-
Storage
- How Do I Expand the Storage Capacity of a Container?
- What Are the Differences Among CCE Storage Classes in Terms of Persistent Storage and Multi-Node Mounting?
- Can I Create a CCE Node Without Adding a Data Disk to the Node?
- What Should I Do If the Host Cannot Be Found When Files Need to Be Uploaded to OBS During the Access to the CCE Service from a Public Network?
- How Can I Achieve Compatibility Between ExtendPathMode and Kubernetes client-go?
- Can CCE PVCs Detect Underlying Storage Faults?
- What Should I Do If a Yearly/Monthly EVS Disk Cannot Be Automatically Created?
- Namespace
-
Chart and Add-on
- What Should I Do If Installation of an Add-on Fails and "The release name is already exist" Is Displayed?
- How Do I Configure the Add-on Resource Quotas Based on Cluster Scale?
- How Can I Clean Up Residual Resources After the NGINX Ingress Controller Add-on in the Unknown State Is Deleted?
- Why TLS v1.0 and v1.1 Cannot Be Used After the NGINX Ingress Controller Add-on Is Upgraded?
-
API & kubectl FAQs
- How Can I Access a Cluster API Server?
- Can the Resources Created Using APIs or kubectl Be Displayed on the CCE Console?
- How Do I Download kubeconfig for Connecting to a Cluster Using kubectl?
- How Do I Rectify the Error Reported When Running the kubectl top node Command?
- Why Is "Error from server (Forbidden)" Displayed When I Use kubectl?
- DNS FAQs
- Image Repository FAQs
- Permissions
- Videos
Show all
Workload Scaling Mechanisms
CCE supports HPA and CustomedHPA policies for workload scaling. The following table describes the differences between these two types of policies.
Item |
HPA Policy |
CustomedHPA Policy |
---|---|---|
Implementation |
Kubernetes Horizontal Pod Autoscaling |
Enhanced auto scaling capabilities |
Rules |
Scales Deployments based on metrics (CPU usage and memory usage). |
Scales Deployments based on metrics (CPU usage and memory usage) or at a periodic interval (a specific time point every day, every week, every month, or every year). |
Enhancement |
Adds the application-level cooldown time window and scaling threshold functions based on the Kubernetes HPA. |
Metric-based:
Periodic: You can select a specific time point every day, every week, every month, or every year or a period as the trigger time. |
Scaling policy priority: If you do not manually adjust the number of pods, auto scaling policies will take effect for resource scheduling. If manual scaling is triggered, auto scaling policies will be temporarily invalid.
How HPA Works
HPA is a controller that controls horizontal pod scaling. HPA periodically checks the pod metrics, calculates the number of replicas required to meet the target values configured for HPA resources, and then adjusts the value of the replicas field in the target resource object (such as a Deployment).
A prerequisite for auto scaling is that your container running data can be collected, such as number of cluster nodes/pods, and CPU and memory usage of containers. Kubernetes does not provide such monitoring capabilities itself. You can use extensions to monitor and collect your data. CCE integrates Prometheus and Metrics Server to realize such capabilities:
- Prometheus is an open-source monitoring and alarming framework that can collect multiple types of metrics. Prometheus has been a standard monitoring solution of Kubernetes.
- Metrics Server is a cluster-wide aggregator of resource utilization data. Metrics Server collects metrics from the Summary API exposed by kubelet. These metrics are set for core Kubernetes resources, such as pods, nodes, containers, and Services. Metrics Server provides a set of standard APIs for external systems to collect these metrics.
HPA can work with Metrics Server to implement auto scaling based on the CPU and memory usage. It can also work with Prometheus to implement auto scaling based on custom monitoring metrics.
Figure 1 shows how HPA works.
Two core modules of HPA:
- Data Source Monitoring
The community provided only CPU- and memory-based HPA at the early stage. With the population of Kubernetes, developers need more custom metrics or monitoring information at the access layer for their own applications, for example, the QPS of the load balancer and the number of online users of the website. In response, the community defines a set of standard metric APIs to provide services externally through these aggregated APIs.
- metrics.k8s.io provides monitoring metrics related to the CPU and memory of pods and nodes.
- custom.metrics.k8s.io provides custom monitoring metrics related to Kubernetes objects.
- external.metrics.k8s.io provides metrics that come from external systems and are irrelevant to any Kubernetes resource metrics.
- Scaling Decision-Making Algorithms
The HPA controller calculates the scaling ratio based on the current metric values and desired metric values using the following formula:
desiredReplicas = ceil[currentReplicas x (currentMetricValue/desiredMetricValue)]
For example, if the current metric value is 200m and the target value is 100m, the desired number of pods will be doubled according to the formula. In practice, pods may be constantly added or reduced. To ensure stability, the HPA controller is optimized from the following aspects:
- Cooldown interval: In v1.11 and earlier versions, Kubernetes introduced the startup parameters horizontal-pod-autoscaler-downscale-stabilization-window and horizontal-pod-autoScaler-upscale-stabilization-window to indicate the cooldown intervals after a scale-in and scale-out, respectively, in which no scaling operation will not be performed. In versions later than v1.14, the scheduling queue is introduced to store all decision-making suggestions detected within a period of time. Then, the system makes decisions based on all valid decision-making suggestions to minimize changes of the desired number of replicas to ensure stability.
- Tolerance: It can be considered as a buffer zone. If the pod number changes can be tolerated, the number of pods remains unchanged.
Use the formula: ratio = currentMetricValue/desiredMetricValue
When |ratio – 1.0| ≤ tolerance, scaling will not be performed.
When |ratio – 1.0| > tolerance, the desired value is calculated using the formula mentioned above.
The default value is 0.1 in the current community version.
The HPA performs scaling based on metric thresholds. Common metrics include the CPU and memory usage. You can also set custom metrics, such as the QPS and number of connections, to trigger scaling. However, metric-based scaling brings in latency of minutes generated during data collection, determination, and scaling phases. Such latency may cause high CPU usage and slow response. To solve this problem, CCE allows you to configure scheduled policies to scale resources regularly for applications with periodic changes.
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