Antyxsoft Kubernetes, managed for you
Seamlessly manage, scale, and automate your containerized applications, all while ensuring reliability and high availability.
Unleash the full potential of your cloud infrastructure
Embrace the future of cloud computing and experience unprecedented efficiency, innovation, and ease of use. Elevate your cloud strategy with Kubernetes and set your business on a path to success in the digital era.
Container orchestration
Kubernetes excels in managing containers, such as Docker containers. It handles tasks like container scheduling, scaling, and load balancing, ensuring that your applications run smoothly and reliably.
Automated scaling
Kubernetes offers automatic scaling capabilities based on resource utilization or custom metrics, ensuring that your applications can adapt to changing workloads and traffic.
Declarative configuration
You describe your application’s desired state in configuration files (YAML) and Kubernetes works to make the actual state match the desired state. This declarative approach simplifies application management.
Storage orchestration
Kubernetes supports various storage solutions, both for persistent storage and temporary storage needs, making it suitable for stateful applications.
Rolling updates & rollbacks
Kubernetes enables rolling updates for applications, allowing you to update your applications with minimal disruption. If issues arise, you can quickly roll back to a previous version.
Service discovery & load balancing
Kubernetes provides built-in service discovery and load balancing for containers, making it easy to expose your services and distribute traffic.
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Antyxsoft Kubernetes
Self-Healing
The ClusterAPI provides self healing when a Kubernetes node fails, spawning new Instances of the pod on a new node, as native Kubernetes does not have the ability of provisioning new infrastructure when this occurs. Since ClusterAPI manages infra-structure and Kubernetes at the same time, it can throw more re-sources onto CloudStack when a failure occurs. The Machine-HealthCheck controller is used to monitor and certify the health of the control plane of the work nodes, guaranteeing the integrity of the services. When a node fails, or resources are insufficient, or the services are unavailable, a new node is provisioned and added to the cluster. In this case, Kubernetes will reschedule the pods from failed nodes.
Self-Healing
The ClusterAPI provides self healing when a Kubernetes node fails, spawning new Instances of the pod on a new node, as native Kubernetes does not have the ability of provisioning new infrastructure when this occurs. Since ClusterAPI manages infra-structure and Kubernetes at the same time, it can throw more re-sources onto CloudStack when a failure occurs. The Machine-HealthCheck controller is used to monitor and certify the health of the control plane of the work nodes, guaranteeing the integrity of the services. When a node fails, or resources are insufficient, or the services are unavailable, a new node is provisioned and added to the cluster. In this case, Kubernetes will reschedule the pods from failed nodes.
Multiple Cluster Management
In case of Kubernetes cluster expansion, ClusterAPI provides support for a range of different providers, including Apache CloudStack. This ClusterAPI capability is important for compa nies that use heterogeneous providers from different service providers. The ClusterAPI abstracts away the different deploy-ment mechanisms that are offered by various providers and infrastructure vendors, allowing operators to fully standardize the entire deployment, regardless of vendor or datacenter or edge. As a result, operators have more control over the entire application environment in a standardized approach to cluster lifecycle management, enabling reuse of existing components across multiple clusters, thus reducing rework.
Multiple Cluster Management
In case of Kubernetes cluster expansion, ClusterAPI provides support for a range of different providers, including Apache CloudStack. This ClusterAPI capability is important for compa nies that use heterogeneous providers from different service providers. The ClusterAPI abstracts away the different deploy-ment mechanisms that are offered by various providers and infrastructure vendors, allowing operators to fully standardize the entire deployment, regardless of vendor or datacenter or edge. As a result, operators have more control over the entire application environment in a standardized approach to cluster lifecycle management, enabling reuse of existing components across multiple clusters, thus reducing rework.
Scaling
ClusterAPI facilitates scaling up and down Kubernetes clusters when workloads change. The main task of ClusterAPI is to ensure that there is enough capacity to meet the current demand for access to the application, also guaranteeing redundancy so that, if a control plane fails, another can attend. With the Kubeadm Control Plane provider (KCP), the operator can declar-atively expand the Kubernetes control plane, thus managing availability and ensuring that the control nodes are organized, minimizing failures during the cluster lifecycle. For worker nodes, just specify the number of nodes; the clusterAPI will provision the new CloudStack Instances and add them to the cluster. When using the Cluster Autoscaler, the number of worker nodes is au-tomatically adjusted to the number of pods needed, thus meet-ing the access demand. Metrics can refer to application work-load or average CPU usage for cluster tuning.
Scaling
ClusterAPI facilitates scaling up and down Kubernetes clusters when workloads change. The main task of ClusterAPI is to ensure that there is enough capacity to meet the current demand for access to the application, also guaranteeing redundancy so that, if a control plane fails, another can attend. With the Kubeadm Control Plane provider (KCP), the operator can declar-atively expand the Kubernetes control plane, thus managing availability and ensuring that the control nodes are organized, minimizing failures during the cluster lifecycle. For worker nodes, just specify the number of nodes; the clusterAPI will provision the new CloudStack Instances and add them to the cluster. When using the Cluster Autoscaler, the number of worker nodes is au-tomatically adjusted to the number of pods needed, thus meet-ing the access demand. Metrics can refer to application work-load or average CPU usage for cluster tuning.
High Performance, Low Price
Scale up or down your resources on demand, ensuring optimal performance without the need for costly hardware investments. Choose the plan that best suits your needs, get incredible performance and ensure that your bill is never a surprise
Frequently Asked Questions
Browse our FAQs to find information about Antyxsoft cloud compute, its features, pricing, billing etc
Which plan of cloud compute should I choose?
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Can I create my own cloud compute plan?
Can I modify the resources of my cloud compute?
What is the SLA provided?
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