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BEER & DEVOPS
NOVEMBER 24 - 2016
M A K E S I T W O R K
Welcome
Auto-scale use-cases
Auto-scale demo
Break
Kontena demo & presentation
Mingling
AGENDA
M A K E S I T W O R K
M A K E S I T W O R K
TRUSTED PARTNER FOR RUNNING BUSINESS
CRITICAL ICT SERVICES
M A K E S I T W O R K
DATACENTER & CLOUD
ADOPTION PHASES
MATURITY
BENEFITS
RESOURCE GROUP
MINIMUM SIZE / MAXIMUM SIZE
RESOURCE GROUP
HORIZONTAL SCALING
M A K E S I T W O R K
AUTOSCALE
WHAT IT IS?
TRIGGERS
RESOURCE LOAD (CEILOMETER)
EXTERNAL EVENT (HTTP POST)
M A K E S I T W O R K
Known variable load Your VDI infrastructure
Unpredictable load Finland Tax information
Steadily increasing load Airbnb
Batch load Payroll processing
AUTOSCALE USE CASE
M A K E S I T W O R K
SCHEDULED OR ON-DEMAND
ORCHESTRATION USE CASE
Disaster recovery Recover to new another site
Ad-hoc Try new plug-in
Duplicate Clone DEV/QA environment
Batch load Software build process
M A K E S I T W O R K
M A K E S I T W O R K
AZ1 AZ2
Application
Tier
Web Tier
Data Tier
www.acme.com
Load Balancing
SCALABLE WEB APP
M A K E S I T W O R K
PROVISION SERVERS/STACK
AUTO ADD NODES TO DATADOG
(SCHEDULED) CREATE LOAD
AUTO-SCALE UP
(SCHEDULED) DECREASE LOAD
AUTO-SCALE DOWN
DEMO
M A K E S I T W O R K
Sum of
CPU LOAD
Listing of
nodes
Listing of
alarms
Listing of
stacks
M A K E S I T W O R K
asg:
type: OS::Heat::AutoScalingGroup
properties:
min_size: 1
max_size: 3
resource:
type: OS::Nova::Server
properties:
key_name: {get_param: key_name}
flavor: {get_param: flavor}
image: {get_param: image}
availability_zone: {get_param: availability_zone}
networks:
- network: { get_param: dmz_network }
metadata: {"metering.stack": {get_param: "OS::stack_id"}}
user_data_format: RAW
user_data:
get_resource: cloud_config_stress
scale_up_policy:
type: OS::Heat::ScalingPolicy
properties:
adjustment_type: change_in_capacity
auto_scaling_group_id: {get_resource: asg}
cooldown: 60
scaling_adjustment: 1
scale_down_policy:
type: OS::Heat::ScalingPolicy
properties:
adjustment_type: change_in_capacity
RESOURCE GROUP
MINIMUM SIZE
RESOURCE GROUP
SCALE OUT SIZE
CEILOMETER
METRICS
ALARM
HEAT ENGINE
RESOURCE POLICY
USER DATA
WEBHOOK
M A K E S I T W O R K
AUTOSCALE
SIMPLIFIED
METRICS
M A K E S I T W O R K
HOW HARD CAN IT BE?
We built this demo from
‘scratch’ in ~3 hours
including planning,
registration to Datadog
and recording
DEMO COSTS
Extra 4GB RAM Server*2
20 minutes
TOTAL = 0,05€
M A K E S I T W O R KM A K E S I T W O R K
STANDARDIZED WORKLOADS – SCALE OUT, NOT UP
HOW MUCH TO CHANGE AT EACH TIME
DATA HANDLING
CONFIGURATION MANAGEMENT
CONSIDERATIONS

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Tech Thursday - Beer & DevOps 24.11.

  • 2. M A K E S I T W O R K Welcome Auto-scale use-cases Auto-scale demo Break Kontena demo & presentation Mingling AGENDA
  • 3. M A K E S I T W O R K
  • 4. M A K E S I T W O R K TRUSTED PARTNER FOR RUNNING BUSINESS CRITICAL ICT SERVICES
  • 5. M A K E S I T W O R K DATACENTER & CLOUD ADOPTION PHASES MATURITY BENEFITS
  • 6. RESOURCE GROUP MINIMUM SIZE / MAXIMUM SIZE RESOURCE GROUP HORIZONTAL SCALING M A K E S I T W O R K AUTOSCALE WHAT IT IS? TRIGGERS RESOURCE LOAD (CEILOMETER) EXTERNAL EVENT (HTTP POST)
  • 7. M A K E S I T W O R K Known variable load Your VDI infrastructure Unpredictable load Finland Tax information Steadily increasing load Airbnb Batch load Payroll processing AUTOSCALE USE CASE
  • 8. M A K E S I T W O R K SCHEDULED OR ON-DEMAND ORCHESTRATION USE CASE Disaster recovery Recover to new another site Ad-hoc Try new plug-in Duplicate Clone DEV/QA environment Batch load Software build process M A K E S I T W O R K
  • 9. M A K E S I T W O R K AZ1 AZ2 Application Tier Web Tier Data Tier www.acme.com Load Balancing SCALABLE WEB APP
  • 10. M A K E S I T W O R K PROVISION SERVERS/STACK AUTO ADD NODES TO DATADOG (SCHEDULED) CREATE LOAD AUTO-SCALE UP (SCHEDULED) DECREASE LOAD AUTO-SCALE DOWN DEMO
  • 11. M A K E S I T W O R K Sum of CPU LOAD Listing of nodes Listing of alarms Listing of stacks
  • 12. M A K E S I T W O R K asg: type: OS::Heat::AutoScalingGroup properties: min_size: 1 max_size: 3 resource: type: OS::Nova::Server properties: key_name: {get_param: key_name} flavor: {get_param: flavor} image: {get_param: image} availability_zone: {get_param: availability_zone} networks: - network: { get_param: dmz_network } metadata: {"metering.stack": {get_param: "OS::stack_id"}} user_data_format: RAW user_data: get_resource: cloud_config_stress scale_up_policy: type: OS::Heat::ScalingPolicy properties: adjustment_type: change_in_capacity auto_scaling_group_id: {get_resource: asg} cooldown: 60 scaling_adjustment: 1 scale_down_policy: type: OS::Heat::ScalingPolicy properties: adjustment_type: change_in_capacity
  • 13. RESOURCE GROUP MINIMUM SIZE RESOURCE GROUP SCALE OUT SIZE CEILOMETER METRICS ALARM HEAT ENGINE RESOURCE POLICY USER DATA WEBHOOK M A K E S I T W O R K AUTOSCALE SIMPLIFIED METRICS
  • 14. M A K E S I T W O R K HOW HARD CAN IT BE? We built this demo from ‘scratch’ in ~3 hours including planning, registration to Datadog and recording DEMO COSTS Extra 4GB RAM Server*2 20 minutes TOTAL = 0,05€
  • 15. M A K E S I T W O R KM A K E S I T W O R K STANDARDIZED WORKLOADS – SCALE OUT, NOT UP HOW MUCH TO CHANGE AT EACH TIME DATA HANDLING CONFIGURATION MANAGEMENT CONSIDERATIONS