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Difference between revisions of "Auto-scaling SIG/Theory of Auto-Scaling"

m (Components of Auto-Scaling)
(Conceptual Diagram)
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[[File:OpenStack-Auto-Scaling.svg|Auto-Scaling Architecture Component Diagram]]
[[File:OpenStack-Auto-Scaling.svg|Auto-Scaling Architecture Component Diagram]]
If you prefer PlantUML
cloud Cloud\n {
  rectangle host as "Host" {
  rectangle host2 as "Host" {
    agent VM
    agent VM2 as "VM"
    agent Container
    agent Container2 as "Container"
agent MS as "Monitoring Service"
agent DS as "Decision Services\n(Clustering,\nOptimization,\nRoot Cause)"
agent Heat as "Orchestration \nEngine"
host -down-> MS
VM -down-> MS
Container -down-> MS : "Metric \nSamples"
MS -down-> DS : "Alarms"
MS -down-> Heat : "Alarms"
DS -right-> Heat : "Scaling Commands"
Heat -up-> host : "Orchestration"
Heat -up-> VM2 : "Orchestration"
Heat -up-> Container2 : "Orchestration"
== Components of Auto-Scaling ==
== Components of Auto-Scaling ==

Revision as of 23:19, 15 May 2019

Theory of Auto-Scaling

General Description

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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
  • 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
  • Don't expect instantaneous scaling (see above)
  • Be aware of where the logic for scaling is (alarm thresholds, decision services)

Anecdotes and Stories