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Condé Nast Italy: Serverless Cost Optimization

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How Condé Nast went form $$$ to $$$/4

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Condé Nast Italy: Serverless Cost Optimization

  1. 1. Serverless Hamburg – 12 March 2018 Serverless Cost Optimization: how Condé Nast went from $$$ to $$$/4 Marco Viganò Digital CTO Serverless On Stage #10 20 March 2018
  2. 2. Serverless Hamburg – 12 March 2018
  3. 3. Serverless Hamburg – 12 March 2018
  4. 4. Serverless Hamburg – 12 March 2018 CN.numbers // by month 30M Unique Visitors 250M Page Views 20% Desktop 80% Mobile 46% SEO 29% Social
  5. 5. Serverless Hamburg – 12 March 2018 CN.technologies
  6. 6. Serverless Hamburg – 12 March 2018 • Infrastructure scaling problems due to traffic boost • Non optimal delivery and uptime • Aggressive time to market • No automation • Costs onPremise(CN) = Error 500 Internal Server Error 2013 / 2014
  7. 7. Serverless Hamburg – 12 March 2018 Wave 1: the pilot
  8. 8. Serverless Hamburg – 12 March 2018 CN.pilot === “Wired.it” MigrationPreparationEvaluation Tuning Pilot Cloud migrationEngagement of team
  9. 9. Serverless Hamburg – 12 March 2018 • Infrastructure migrated AS IS -> no optimization for the cloud • 150 Server + 30 DB + more than 50 LB • Application redundancy • Costs explosion: CN #epicfail on premise + cloud + people + external providers = _______________ a lot of money!!!
  10. 10. Serverless Hamburg – 12 March 2018 Wave 2: consolidation (start Q3 2014)
  11. 11. Serverless Hamburg – 12 March 2018 • Application !cloud optimized • Destroy monoliths • Make refactoring • Automation • CN Blueprint
  12. 12. Serverless Hamburg – 12 March 2018 CN.blueprint
  13. 13. Serverless Hamburg – 12 March 2018 end of wave 2 (2015): ROI
  14. 14. Serverless Hamburg – 12 March 2018 Wave 3: thinking Serverless (start Q3 2014)
  15. 15. Serverless Hamburg – 12 March 2018 CN.Vogue().photovogue • > 300,000 photographers more than 800,000 photos image size up to 50 Mb The Challenge • PV was launched in 2011: needs new UI/UX and to be re-engineered • Photos and users growing by the day: old legacy IT infrastructure wasn’t able to manage the website traffic • We need to provision resources quickly: problems in scaling • We wanted to give both photographers and editorial staff a better, faster experience • Problems with large file upload
  16. 16. Serverless Hamburg – 12 March 2018 serverless(CN.Vogue().photovogue) • Quicker provisioning of resources: from days to hours • No scaling problem due to traffic boost • Cost saving: cut 30% in comparison to the old infrastructure • Enabling innovation: Devs / DevOps, are now focused on innovation not on manage old infrastructure survival • UX 90% faster: photographer and editorial team now have an excellent experience old_windows_cluster(CN.Vogue().photovogue)
  17. 17. Serverless Hamburg – 12 March 2018 end of wave 3 (2016)
  18. 18. Serverless Hamburg – 12 March 2018 Wave 4: reserve capacity (start in Q2 2016 – running in 2017/2018)
  19. 19. Serverless Hamburg – 12 March 2018 Predictable Workloads
  20. 20. Serverless Hamburg – 12 March 2018 • Reserved Instances / Committed use discounts • 1 year / 3 years • CN.Italy.saving[‘2016’] = 35% • CN.Italy.saving[‘2017’] = 60% • CN.Italy.saving[‘2018’] = VMs + DB + DWHPay as yo go Reserved Capacity
  21. 21. Serverless Hamburg – 12 March 2018 2017 costs = On Premise / 4
  22. 22. Serverless Hamburg – 12 March 2018 Wave 5: container (start Q4 2017 - runninng)
  23. 23. Serverless Hamburg – 12 March 2018 • Build, Ship, and Run any App, Anywhere • Running containers across many different machines • Scaling up or down by adding or removing containers when demand changes • Keeping storage consistent with multiple instances of an application • Distributing load between the containers • Launching new containers on different machines if something fails
  24. 24. Serverless Hamburg – 12 March 2018
  25. 25. Serverless Hamburg – 12 March 2018 Tips & Tricks
  26. 26. Serverless Hamburg – 12 March 2018 • VMs Autoscaling • Caching: Varnish, Redis, memcache… • Offload of static resources: CDN • Infrastucture self healing • Serverless Tip&Tricks(CN); // costs optimization
  27. 27. Serverless Hamburg – 12 March 2018 Turn off the lights
  28. 28. Serverless Hamburg – 12 March 2018
  29. 29. Serverless Hamburg – 12 March 2018 Turn off the lights 25% 25% 25% 25% • CPU from 8pm to 8am • 0.2$/h 0.2$/h x 4VMs x 24h x 365day = 7008 $ • Turn of from 8pm to 8am 12h x 365day = 4380h saving = 876$ • 7008$ - 876$ = 6132$ • 12.5% Saving 33% 33% 33%
  30. 30. Serverless Hamburg – 12 March 2018
  31. 31. Serverless Hamburg – 12 March 2018 Last but not least • Make investments on your team: training, summit, Meetup, certifications, R&D… • Your team must be at the center of your Cloud Transformation • No Team -> No Party -> No Saving!!!
  32. 32. Serverless Hamburg – 12 March 2018 summary
  33. 33. Serverless Hamburg – 12 March 2018 CN.costs.onCloud() = CN.costs.onPremise() / 4 CN.time_to_market.onCloud() = CN.time_to_market.onPremise() / 5
  34. 34. Serverless Hamburg – 12 March 2018 2013/2014 >150 servers! 30 Databases 2015: ROI!!!! 2016 Change Mindset: Thinking Serverless - Photovogue - Starting reducing costs From an angry CFO… to a happy CFO :) 2017 Infrastucture improvements 50 servers - 8 Databases Costs = on premise / 4 On premise 2018 Continuos improvements: Serverless *.* Docker / K8
  35. 35. Serverless Hamburg – 12 March 2018 Thank You Marco Viganò @Sasha0423

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