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Journey to Cloud Native
How to move to the cloud in three steps
• Alois Reitbauer, @aloisreitbauer
about:me
Alois Reitbauer
Chief Strategist
Lead Innovation Lab
(never smiles on photos)
Bimodal IT
Not every project is a cloud project, but cloud thinking is everywhere
Develop
Big monolithic application. Small interconnected purpose-built services.
Pizza Box Teams
Small teams can deliver features into production
New Rules in the Game
You build it, you run it.
Werner Vogels, CTO Amazon
Ship
Deploy
Big Bang Releases of single special built applications. Small continuous service delivery of standardized delivery blocks.
Compute
Hardwired datacenters. Datacenter as an API.
The journey to become cloud-native
We see three different stages how
companies embrace transformation
Stage 1: Continuous Delivery
“Lift and Shift” to modern platforms
Applications stay the same
Release speed massively increases
Writing fast cloud native applications easy
Rule #1: Don’t write slow code
Control code complexity
N+1 interaction patterns - control flow design
Duplicate interactions - bounded context, SPOF
More than 10 service hops - hard to debug
Highly asynchronous - hard to understand
Unusual Log Activity
Continuous Performance Management
Stage 2: Refactoring towards Micro Services
First micro services
Scheduling and Orchestration
Architectures become polyglot
Independent Release Cycles
Writing fast cloud native applications easy
Rule #1: Don’t write slow code
Rule #2: Don’t write code that doesn’t scale
Control Environment Complexity
Service Call Payload - < 500 kb
Chatty services - > 50 service calls
Inter-tier heavy interactions - granularity and locality
Status Codes - only Ok is 200
Understand Micro Service Interactions
Stage 3: Dynamic Micro Services
Up and down scaling near real time
Advanced resilience mechanisms
Infrastructure totally abstracted
Writing fast cloud native applications easy
Rule #1: Don’t write slow code
Rule #2: Don’t write code that doesn’t scale
Rule #3: Understand application behavior
Watch your application live
Health monitoring – symptoms
Orchestration monitoring - causality
Application monitoring - root cause
7:00 a.m.
Low Load and Service running
on minimum redudancy
12:00 p.m.
Scaled up service during peak load
with failover of problematic node
7:00 p.m.
Scaled down again to lower load
and move to different geo location
Deployments are no longer static
You don’t fly by hand here
820 Billion
dependencies
Network Problem
Mushroom cloud effect
Container Network Monitoring
Make it part of your environment
Thank you!
Alois Reitbauer
alois.reitbauer@dynatrace.com
@aloisreitbauer

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The Journey to Becoming Cloud Native – A Three Step Path to Modernizing Applications

Editor's Notes

  1. It’s also about how you compute within your data center or cloud. As you move toward apps of innovation you will also move toward software-defined data centers. If you’re using the public cloud everything is soft-wired already, but maybe in your data centers you’re looking toward VXLAN or other means to turn your current hard-wired networking into software. And not only networking, but also how you provision systems, how you make them accessible on a self-serve basis to groups like your engineering teams. In many ways your data center is turning into an API and you’re becoming a cloud provider. Discussion questions: Are you moving toward self-service for your engineering teams? Do you see your data center team as becoming a cloud operations team?
  2. B2W example. 9 applications are HEALTHY!!!! only services have issues, but thats fine. 3300 servics running on 10k JVMs on just 142 hosts thats no longer for human doing visual monitoring; ist ideal for intelligent monitoring systems to deal with