Applying AI to Performance Engineering: Shift-Left, Shift-Right, Self-Healing
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The document discusses how artificial intelligence can be applied to performance engineering to make it self-healing and self-service. It describes how monitoring needs have evolved from just looking at dashboards and logs to dealing with dynamic cloud environments. It outlines how AI can be used for full-stack monitoring with one agent, automated end-to-end tracing, automated log analytics and change detection. It then discusses how AI can enable shifting work left to break the pipeline earlier, improve mean time to resolution with auto-mitigation, and shift work right with tags, deployments and events to create actionable feedback loops across development, operations and business teams.
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Applying AI to Performance Engineering: Shift-Left, Shift-Right, Self-Healing
- 1. Confidential, Dynatrace, LLC Applying Artificial Intelligence to Performance Engineering – Self-Healing, Self-Service Andreas Grabner - December 2017 @grabnerandi
- 2. Monitoring used to be about looking at dashboards …
- 3. Process Memory (GB) CPU Graphs (%)
- 4. .. and about analyzing logs & exceptions …
- 6. But the apps and services we build have transformed to something more dynamic…
- 7. Develop Ship Deploy Run Scale Compute nodejs mongo db netty cassandra redis ansible jenkins puppet chef docker cloudfoundry rh openshift rh atomic rocket core os rancher kvm busybox mesos marathon kubernetes swarm Amazon azure openstack mesosphere calico weave eureka/hystrix A whole new technology stack & polyglot development Amazon DynamoDB AWS Lambda AWS CodeDeploy Amazon EC2 Container Services Amazon EC2 AWS Elastic Beanstalk Amazon API Gateway
- 8. Cloud OS, Disks Containers, Processes, Logs Application- & Webserver Mobile* Services Network Browser 3rd parties FullStackDiscovery,Modeling&Analytics
- 9. confidential One Agent to monitor them all
- 12. All Timeseries Data you can wish for Network Container Servers Hosts Cloud
- 14. confidential Automated Log Analytics and Change Detection
- 15. Your Apps/ServicesYour Users Dynatrace OneAgent AI Supported Performance Engineering
- 16. Dev Perf/Test Ops Biz Shift-Left: Break Pipeline Earlier Improve MTTR: Auto-Mitigation Shift-Right: Tags, Deploys, Events Actionable Feedback Loops
- 17. Shift-Right: Tags, Deployments & Events docker run –e DT_TAGS=BLUE dtcli tag srv CartServicev2 GREEN dtcli evt push host .*demo version=123 source={git_commit} dtcli evt push pg tomcat1 desc=JVMMemIncr hint=+100MB Dynatrace Smartscape Release Automation Dynatrace Automation API, CLI, Auto-Detection
- 18. Improve MTTR: Automate Mitigate with AI Data Auto Mitigate! 1 CPU Exhausted? Add a new service instance! 3 Issue with BLUE only? Switch back to GREEN! ?Escalate at 2AM? 2 High Garbage Collection? Adjust/Revert Memory Settings! 4 Hung threads? Restart Service! 5 Still ongoing? Initiate Rollback! Escalate ? Still ongoing?5 1 2 3 4 Mark Bad Commits Update Dev Tickets … … Impact Mitigated?? ?
- 19. Shift-Left: Break Pipeline Earlier c0123bd nov17 myservice:nov17 myservice:nov17 space:UAT space:PERF myservice:nov17 Selenium Perf Data space:PERF myservice:nov17myservice:nov16 space:PROD myservice:BLUE myservice:GREEN myservice:nov17 space:PROD myservice:BLUE myservice:GREEN space:PROD myservice:nov16
- 20. Shift-Left: Performance as Self-Service myservice:tmp57 myservice:tmp57 space:PERF c0123bd
- 21. Actionable Feedback Loops: Business Success Criteria Labels become Key User Action Live Data Queries New Requirement Definition
- 22. Filter by tags, versions, … Access to all key metrics Access to every service Actionable Feedback Loops: Architects
- 23. C:dynatrace-cli> py dtcli.py dqlr srv tags/?key=.*prod.* service.requestspermin[count%180:0],service.failurerate[avg%180:0] Actionable Feedback Loops: SRE’s Live data access through REST API/CLI
- 24. Actionable Feedback Loops: Load Testing Dynatrace Data
- 25. Actionable Feedback Loops: Load Testing Extracted from HTTP Header Tag(s) PurePath for Load Test Requests
- 26. Actionable Feedback Loops: Operations Filter by Infrastructure, Service, Application
- 27. Dev Perf/Test Ops Biz Shift-Left: Break Pipeline Earlier Improve MTTR: Auto-Mitigation Shift-Right: Tags, Deploys, Events Actionable Feedback Loops
- 28. confidential Demo Time or Done
- 29. Confidential, Dynatrace, LLC Applying Artificial Intelligence to Performance Engineering Andreas Grabner - November 2017 @grabnerandi
Notas do Editor
- That may have worked well for static environments where you knew what you are looking at
- If your apps gave you logs you could use log analytics to analyze the log files ->in case you knew what to look for and in case the log messages were actually written We could also correlate logs and exceptions to identify strange patterns
- All of this worked well in case the applications were rather static – not too large and you had the people that understood how to analyze data provided by different tools BUT – the world has changed
- These is the new technology stack we are dealing with – and it is by far not complete New players coming and going – allowing us to implement new types of apps with new architectural and deployment options
- But there is more than production! There is more we can do throughout the whole DevOps Toolchain
- But there is more than production! There is more we can do throughout the whole DevOps Toolchain