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From Cloud to Fog: the Tao of IT Infrastructure Decentralization

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Keynote by Dr. Guillaume Pierre, Professor of Computer Science at the University of Rennes 1 (France), at the IEEE CloudNet conference, 4th November 2019.

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From Cloud to Fog: the Tao of IT Infrastructure Decentralization

  1. 1. From Cloud to Fog: The Tao of IT Infrastructure Decentralization Guillaume Pierre Keynote at the IEEE CloudNet Conference, Nov 4th 2019 The Tao of IT Infrastructure Decentralization 1 / 31
  2. 2. End users want interactivity IoT devices produce huge volumes of data The Tao of IT Infrastructure Decentralization 2 / 31
  3. 3. The Tao of IT Infrastructure Decentralization 3 / 31
  4. 4. The Tao of IT Infrastructure Decentralization 3 / 31
  5. 5. Latency Bandwidth Cost The Tao of IT Infrastructure Decentralization 3 / 31
  6. 6. Fog Computing The Tao of IT Infrastructure Decentralization 4 / 31
  7. 7. Fog Computing The Tao of IT Infrastructure Decentralization 4 / 31
  8. 8. Fog Computing The Tao of IT Infrastructure Decentralization 4 / 31
  9. 9. Fog Computing The Tao of IT Infrastructure Decentralization 4 / 31
  10. 10. Fog Computing The Tao of IT Infrastructure Decentralization 4 / 31
  11. 11. Fog = Cloud: the dierence is the network Typical Cloud platform Few data centers High-performance networks Cloud resource location is (mostly) irrelevant Resources close to each other, far from the users Typical Fog platform Lots of points-of-presence Heterogeneous networks Fog resource location is extremely important Resources close to the users, far from each other The Tao of IT Infrastructure Decentralization 5 / 31
  12. 12. Experimentation platform ⇒ the fridge The Tao of IT Infrastructure Decentralization 6 / 31
  13. 13. The Tao of IT Infrastructure Decentralization The Tao of IT Infrastructure Decentralization 7 / 31
  14. 14. The Tao of IT Infrastructure Decentralization The Tao of IT Infrastructure Decentralization 7 / 31
  15. 15. The Tao of IT Infrastructure Decentralization The Tao of IT Infrastructure Decentralization 7 / 31
  16. 16. Kubernetes is a great platform... Container-based ⇒ lightweight, can run on a Raspberry Pi Pods and services ⇒ easy to deploy and (re-)scale applications Integrated autoscaling ⇒ ready for uctuating workloads Designed around feedback control loops ⇒ simple and robust Large community of users and developers ⇒ we can nd help whenever necessary The Tao of IT Infrastructure Decentralization 8 / 31
  17. 17. ... but it is not ready for the fog The Tao of IT Infrastructure Decentralization 9 / 31
  18. 18. ... but it is not ready for the fog The Tao of IT Infrastructure Decentralization 9 / 31
  19. 19. ... but it is not ready for the fog The Tao of IT Infrastructure Decentralization 9 / 31
  20. 20. ... but it is not ready for the fog The Tao of IT Infrastructure Decentralization 9 / 31
  21. 21. ... but it is not ready for the fog The Tao of IT Infrastructure Decentralization 9 / 31
  22. 22. How Kubernetes routes end user requests The Tao of IT Infrastructure Decentralization 10 / 31
  23. 23. How Kubernetes routes end user requests The Tao of IT Infrastructure Decentralization 11 / 31
  24. 24. Kubernetes load-balances trac across pods Let's issue lots of requests on node #7 (which contains a pod) Other pods are available: At node #6: 2ms At nodes #4 and #5: 4ms At nodes #2 and #3: 6ms The Tao of IT Infrastructure Decentralization 12 / 31
  25. 25. If we make request routing location-aware Proximity-Aware Trac Routing in Distributed Fog Computing Platforms. Ali Fahs and Guillaume Pierre. In Proceedings of the IEEE/ACM CCGrid Conference, May 2019. The Tao of IT Infrastructure Decentralization 13 / 31
  26. 26. Great. But. . . We still need to place pods close to the end users The Tao of IT Infrastructure Decentralization 14 / 31
  27. 27. The Tao of IT Infrastructure Decentralization 15 / 31
  28. 28. Pod placement is an NP-hard problem Place r replicas among n fog nodes to minimize user-pod distance ⇒ r n = n! r!(n−r)! possible combinations to explore When n = 10 and m = 100 this makes 17,310,309,456,440 combinations Heuristic: Let's nd any reasonably good pod placement Reasonably good == at least F% of user requests served within X ms (e.g., F = 99.9% and X = 5 ms) We can explore the space of placements (more or less) randomly until: we nd a reasonably good placement or a timer expires The Tao of IT Infrastructure Decentralization 16 / 31
  29. 29. Scheduling pods Create Pods Schedule Pods Deploy Pods Request a Deployment Deployement Controller Pod objects Created Kubernetes Scheduler Pods Assigned Running Pods Kubelet S4 S1 S2 S3 S4 S5 S6 S7 S8 S1 S3 S4 S8 S4 S8 S1 S3 Score: 8 Score: 7 Score: 4 Score: 3 All Schedules Feasible Schedules Ranked Schedules Final  Solution Filter Score Rank Assign The Tao of IT Infrastructure Decentralization 17 / 31
  30. 30. Pod placement performance q q q q q q q q q q q q q q q q n=50 n=100 n=200 n=300 93 95 97 99 98.3 98.8 99.3 99.8 99.61 99.71 99.81 99.91 99.84 99.87 99.90 99.93 K R H1 H2 µP% Algorithm (a) Proximity as function of dierent algorithms q q q q q q q q q q q q q q q q n=50 n=100 n=200 n=300 5 7 9 11 3.4 4.0 4.6 5.2 1.7 2.0 2.3 2.6 1.3 1.5 1.7 K R H1 H2 µI% Algorithm (b) Fairness as function of dierent algorithms The Tao of IT Infrastructure Decentralization 18 / 31
  31. 31. The Tao of IT Infrastructure Decentralization 19 / 31
  32. 32. Stream data processing The Tao of IT Infrastructure Decentralization 20 / 31
  33. 33. Stream data processing The Tao of IT Infrastructure Decentralization 20 / 31
  34. 34. The Tao of IT Infrastructure Decentralization 21 / 31
  35. 35. The Tao of IT Infrastructure Decentralization 21 / 31
  36. 36. With a bandwidth-optimized pre-combined operator The Tao of IT Infrastructure Decentralization 22 / 31
  37. 37. The Tao of IT Infrastructure Decentralization 23 / 31
  38. 38. So Si Op1 Op2 Op3 (a) Logical graph of operators. SiOp3 Op2 So So Op1 Op1 Op1 (b) Replicated data source and operator. (c) Geo-distributed operator replicas in a Fog computing environment. Can we model the performance of stream processing in the fog? The Tao of IT Infrastructure Decentralization 24 / 31
  39. 39. Performance model for a single operator We can model an operator's throughput with just 3 parameters: Πn = α nβ + γ × NDmax α captures the operator's computational complexity β captures the parallelism overhead of Apache Flink γ captures the impact of network latency With this model we can predict the throughput with error ≈ 2% 0 10000 20000 30000 40000 50000 60000 70000 80000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Measured Points ProcessingTime(ms) Number of Replicas Basic Measurments 3 Measurments Prediction Simple operators: replicated and distributed KeyBy operator Model calibration Model composition The Tao of IT Infrastructure Decentralization 25 / 31
  40. 40. The Tao of IT Infrastructure Decentralization 26 / 31
  41. 41. The FogGuru project www.fogguru.eu Training the Next Generation of European Fog Computing Experts H2020 European Industrial Doctorate Maria Skªodowska-Curie project Eight talented PhD students conducting research on fog computing Real-world experimentation in Valencia (Spain), starting. . . today The Tao of IT Infrastructure Decentralization 27 / 31
  42. 42. Real-world experimentation Technical goals: Detect water leaks Optimize water distribution Inform citizens about their consumption . . . ? Societal goals: Validate research in real-life settings Develop fog application blueprints Meet real customers and citizens Benet the city of Valencia, its citizens and its companies Disseminate the project's results The Tao of IT Infrastructure Decentralization 28 / 31
  43. 43. The Tao of IT Infrastructure Decentralization 29 / 31
  44. 44. When a Cloud surrounds you it becomes Fog. This is only the beginning of the journey. . . www.fogguru.eu The Tao of IT Infrastructure Decentralization 30 / 31
  45. 45. Credits The Myriads team at IRISA: The FogGuru project: The Tao of IT Infrastructure Decentralization 31 / 31

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