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Auto-scaling SIG/Theory of Auto-Scaling

Theory of Auto-Scaling

General Description

<fill in> <what is the scope of auto-scaling, how does it differ from self-healing, what does it have in common with self-healing>

Conceptual Diagram

Auto-Scaling Architecture Component Diagram

Components of Auto-Scaling

OpenStack offers a rich set of services to build, manage, orchestrate, and provision a cloud. This gives administrators some choices in how to best serve their customer's needs.

  • Scaling units - There are a number of components that can be controlled with Auto-Scaling.
    • Compute Host
    • VM running on a Compute Host
    • Container running on a Compute Host
    • Network Attached Storage
    • Virtual Network Functions
  • Monitoring Service - either using an agent installed on the Scaling unit, or using a polling method to retrieve metrics
  • Alarming Service
  • Decision Services - There are a number of services in OpenStack that can interpret metrics and alarms based on configured logic and produce commands to Orchestration Engines
    • Congress
    • Heat
    • Vitrage
    • Watcher
  • Orchestration Engines
    • Heat
    • Senlin is a clustering engine for OpenStack, and can orchestrate auto-scaling
    • Tacker

Considerations and Guidelines

  • Monitoring takes resources, plan accordingly
  • Avoid scaling too quickly or too often
    • This can be done by specifying appropriate cooldown periods.
    • Another technique is to average the scaling metric over a longer time period to avoid reacting to sudden fluctuations
  • Don't expect instantaneous scaling (see above)
    • Define thresholds to be predictive of scale needs, not reactive to a bad state
  • Be aware of where the logic for scaling is (alarm thresholds, decision services)
  • Define appropriate scaling limits in terms of minimum and maximum instances.
    • Minimum number of instances will prevent all the instances from being removed.
    • Maximum number of instances safeguards against provisioning too many resources that could adversely affect other workloads.
  • Applications must be horizontally scalable in order to auto-scale the underlying instances.
    • Applications must be stateless or be able to drain existing stateful connections so that the underlying instances can be removed during a scale down.
    • Incoming requests must be dynamically load balanced among the instances running the application.

Anecdotes and Stories