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Using Machine Learning to Optimize DevOps Practices


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Describes machine learning systems and how teams can train these systems using the large amounts of data generated in DevOps practices.

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Using Machine Learning to Optimize DevOps Practices

  1. 1. Using Machine Learning to Optimize DevOps Practices Building Learning into Monitoring and Feedback Peter Varhol
  2. 2. About me • International speaker and writer • Degrees in Math, CS, Psychology • Technology communicator • Former university professor, tech journalist • Cat owner and distance runner •
  3. 3. Agenda • What is machine learning? • How is machine learning applied to DevOps? • Challenges in training these systems • What constitutes an issue? • Summary and conclusions
  4. 4. What is Machine Learning? • Layered algorithms that change parameters based on feedback from know data • Can be linear or nonlinear • Algorithms can be fixed in production or adaptive • Fixed – algorithms do not adjust once deployed • Adaptive – algorithms continually adjust to new data • Usually part of a larger system
  5. 5. Adaptive Systems • Airline pricing • Ticket prices change three times a day based on demand • It can cost less to go farther • It can cost less later • Ecommerce systems • Recommendations try to discern what else you might want • Can I incentivize you to fill up the plane?
  6. 6. Why Use Adaptive? • The “right” result will vary over time • Trying to optimize a particular result • Revenue • The problem domain is not static Confidential, Dynatrace LLC
  7. 7. How Are Fixed Systems Used? • Transportation • Self-driving cars • Aircraft/Drones • Ecommerce • Recommendation engines • Medical • Diagnosis systems
  8. 8. Why Use Fixed Machine Learning Systems • The problem domain is static • The expectations remain constant • The right answer is known under most conditions • The original algorithms remain valid over a long period of time
  9. 9. DevOps Practices Generate Data • During development • Agile metrics, JIRA issues, test case metrics • During continuous integration • System test metrics • During continuous deployment • Quality metrics for deployments • After deployment and into production • Application availability and performance • Usage log files
  10. 10. Focus on Monitoring • Ongoing data on availability and performance • RUM • Synthetic tests • Application monitoring • Monitoring tackles the back end of DevOps • Identifying unhealthy trends • Diagnoses failures and poor performance • Recommends action • Fixed or adaptive depends on your goals
  11. 11. Where Do Predictive Analytics Come In? • Big data makes possible predictions of future events • Are we going to fail? • How will we perform with traffic surges? • As well as past events • What went wrong and how do we fix it • We can rely on past data • Adaptive systems may not perform as well • Clear goals needed
  12. 12. What Technologies Are Involved? • Neural networks • Genetic algorithms • Rules engines
  13. 13. Neural Networks • Set of layered algorithms whose variables can be adjusted via a learning process • The learning process involves training with known inputs and outputs • The algorithms adjust coefficients to converge on the correct answer (or not) • You freeze the algorithms and coefficients, and deploy • Or you optimize on a particular set of characteristics
  14. 14. A Sample Neural Network
  15. 15. Genetic Algorithms • Use the principle of natural selection • Create a range of possible solutions • Try out each of them • Choose and combine two of the better alternatives • Rinse and repeat as necessary
  16. 16. Bringing in DevOps • DevOps has data that can be used to train neural networks • Health of the application • Trends in application traffic and responsiveness • Application failure
  17. 17. Machine Learning Helps DevOps • Decisions are complex • Why is the CPU maxed? • What is causing disk thrashing? • Why did the network slow? • Why did the application fail? • Data is massive • Potentially thousands of data points a day
  18. 18. How Good Are Decisions? • Expert versus machine • Given the same data • In many domains they tie • With additional data, the human can be better • But machine learning will get better • But only as good as the data
  19. 19. We Want to Do Two Things • Identify trends that may indicate future problems • Increasing response times • More page errors • Diagnose faults once they have happened • Why did the application fail? • How can we fix it as quickly as possible?
  20. 20. Fixed Algorithms Work for Some Problems • Immediate performance and failure identification • Diagnosis of failures and performance issues • These are readily identifiable from known data
  21. 21. Adaptive Systems Supplement These Tools • Predictions of future events • Performance • Availability • The target is moving • So we need current data to adjust the algorithms
  22. 22. The Machine Helps the DevOps Expert • The machine learning app provides: • Early warning on possible performance issues and failures • Immediate notification of failure or impending failure • Trend analysis of data to predict unhealthy outcomes • The machine learning is an assistant • It can’t fix anything • It can’t necessarily identify the root cause
  23. 23. What is the Goal? • We have many ways of monitoring • Many of them are represented at this conference • Each measures something a little different • Latency, response time, availability, network, DNS . . . • Too much data can be no better than no data at all • Machine learning can correlate across measurements • Focus to eliminate false positives
  24. 24. Intelligent Systems Are Sometimes Wrong • The problem domain is ambiguous • There is no single “right” answer • “Close enough” is good • We don’t know quite why the software responds as it does • We can’t easily trace code paths
  25. 25. Testing Machine Learning Systems • Have objective acceptance criteria • Test with new data • Don’t count on all results being accurate • Understand the architecture of the network as a part of the testing process • Communicate the level of confidence you have in the results to management and users
  26. 26. A Cautionary Tale • All events are not created equal • AI systems treat events equally • A failure of a system during busy season is the same as any other • DevOps pros know otherwise • And can exert additional effort in response • And actually fix the problem • We can’t automate what we don’t understand • You need the human in the loop Confidential, Dynatrace LLC
  27. 27. Conclusions • DevOps is a natural environment for machine learning systems • Any activity that generates data and requires a decision is fair game • Monitoring is low-hanging fruit • Fixed systems for failure and diagnosis, adaptive for trend analysis Confidential, Dynatrace LLC
  28. 28. References • whos-to-blame/ • our-ai-revolution/ • the-clutch/ Confidential, Dynatrace LLC
  29. 29. Thank You Peter Varhol