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Auto Scaling (AS)

The Auto Scaling (AS) service ensures that web services are available at all times. It automatically starts additional resources when the system load increases, e.g. when a service receives increasing numbers of queries. At the same time, it detects when too many resources have been provided and deactivates them, saving you costs. Auto Scaling works on the basis of rules set during its installation.

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Reasons for AS in the Open Telekom Cloud

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Data protection

The automatically scaled instances in the Elastic Cloud Server (ECS) are operated in the EU under European management and are GDPR compliant. Non-European authorities have no access.

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Ease of use

With Auto Scaling, you can use pre-configured AS policies to automatically scale service resources with just a few clicks. Manual downscaling and
upscaling of instances is now a thing of the past.

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Flexibility

Scheduled, periodic or alarm policies and capacity thresholds can be configured to automatically increase or decrease the number of Elastic Cloud
Server (ECS) instances.


Key Features of AS

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Transparency

The alarm-triggered AS policy interacts with Cloud Eye Service (CES) and provides historical views for AS groups. CES monitors the utilization of the allocated vCPU and RAM resources.

 
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Free use

The AutoScaling service is free, but the generated resources (ECS VMs) are chargeable.

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Automation

A high level of automation is possible through dynamic, planned policies and API support for Terraform, Ansible, etc.

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High availability

Together with Elastic Load Balancer (ELB), Auto Scaling offers a highly available solution.

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Fault tolerance

AS enables horizontal scaling (scale in/out) and monitoring of instances to replace unhealthy instances.

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Cost management

Dynamic, scheduled or manual scaling options, as required, improve cost management on demand.


Use Case

Auto-Scaling can be used to deploy a distributed application system on a cloud platform to centrally manage distributed clusters and scale resources based on planned AS actions or automatically based on monitoring data.

Advantages:

  • Automatical attachment of instances to load balancer
  • Monitoring of the instances' health states
  • Alarm-triggered, scheduled, and periodic policies

Example scenarios:

Web applications | Big data and high-performance computing

Graphic of the use cases
 

Neue Features

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