A stable, secure, efficient, elastically scalable, and easy-to-use Kubernetes container management platform
Overview
Born out of the open-source Kubernetes system, TKE provides container-centric, highly scalable, and high-performance container management services. TKE is fully compatible with native Kubernetes APIs and has extension Kubernetes plugins, such as CBS and CLB. TKE provides containerized applications with a comprehensive set of features that provide efficient deployment, resource scheduling, service discovery, and dynamic scaling.
TKE is fully compatible with the entire range of Kubernetes capabilities and has been adapted to the fundamental IaaS capabilities of UIHCloud, such as Cloud Virtual Machine (CVM) and Cloud Block Storage (CBS). In addition, CBS, CLB, and other Kubernetes-based cloud products provided by UIHCloud support rapid deployment of container clusters for a variety of open-source applications, which greatly improves deployment efficiency.
TKE operates within your own CVM instance. Computing resources are not shared with other instances. Your clusters run in VPCs and allow you to use custom security groups and network ACLs. TKE uses a distributed service architecture to implement automatic recovery rapid service migration. TKE works with distributed backend storage of stateful services to guarantee high availability of services and the security of data.
TKE simplifies the management of large-scale clusters, and the management and OPS of distributed applications. You do not need to use cluster management software or design fault-tolerant cluster architecture. Launch TKE and specify the tasks you want to run. TKE manages the cluster management tasks and allows you to focus on development of Dockerized applications.
TKE runs on a high-quality BGP network. TKE enables instant downloads and uploads of images, rapid launch of massive amounts of containers in seconds, and higher container deployment efficiency. TKE can quickly build, test, and package submitted business code and deploy the integrated code to pre-release and production environments.
TKE supports elastic cluster hosting. This makes it easy to schedule long-running applications and batch jobs to expand your business. TKE also integrates Cloud Load Balancer, allows traffic distribution among containers, automatically recovers unhealthy containers, and ensures that the number of containers meets your needs for application support.
TKE is free to use. You can build your cluster manager by calling the API in the container free of charge. You only need to pay for cloud service resources that you create for storage and running applications, such as CVM and CBS resources.
The microservice architecture is suitable for creating complex applications. The microservice architecture splits your monolithic application into multiple microservices across different dimensions. Then, the content of each microservice can be managed by a Docker image. An application is split into multiple manageable services without changing its features. Each individual service can be easily understood, developed and maintained. Different microservices can be developed by different teams. Teams can choose their own development techniques and programming languages. This allows each service to be deployed and expanded separately.
For example, a web application can be split into a group of simpler external services and multiple groups of internal services.
The best-in-class DevOps environment enabled by continuous integration and delivery can greatly improve the release efficiency of software. Continuous integration allows developers to complete build and (unit) test processes immediately after new code is submitted to determine whether the new code and the original code can be properly integrated. Continuous delivery allows developers to deploy the integrated code to pre-release or existing network environments based on continuous integration.
TKE provides auto scaling capabilities at the cluster and service levels. TKE can automatically scale up or down by monitoring container metrics such as CPU, memory, and bandwidth. Meanwhile, it can automatically scale the cluster according to the deployment conditions of the container when resources are insufficient or excessive.
