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Big Data @ NT - A Network Technology Perspective

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This presentation was given at our Big Data Days Event. The presentation provides a comprehensive (albeit high level) view of the important parts of Big Data in Network Technology.

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Big Data @ NT - A Network Technology Perspective

  1. 1. Big Data @ NT Network Technology Perspective Big Data Day Frankfurt am Main, Germany September 22nd, 2016 Dr. Kim Kyllesbech Larsen, Group Technology, Deutsche Telekom.
  2. 2. Dr. Kim K. Larsen / Big Data @ NT 2 Big Data is Big Team Work.
  3. 3. Big Data @ NT. Dr. Kim K. Larsen / Big Data @ NT 3 Strategy & Vision.  We call it the “Right-in-Time Big Data Architecture”.  Serves all needs for foreseeable future (i.e., min. next 5 yrs).  Supports all existing & proposed new network use cases.  Supports Real Time and non-Real Time Technology use cases. Alignment with IT & Segments.  Fully aligned over-arching Big Data architectural principles.  High degree of synergy with IT and other segments embracing open source solutions.  New components required by Network have been identified & conceptually aligned with IT.
  4. 4. Big Network Data. An illustration … Dr. Kim K. Larsen / Big Data @ NT 4 Timing Action & Reaction High Velocity (events/sec) Large Variety (e.g., 10k+ event cats) Very High Volume Event Process approx. <1+> Alarm per sec approx. <30+> Events per millisecond Daily (mobile) IP User Plane Data 750+ Tera Byte approx. 20 Mega Byte per millisecond
  5. 5. Future of Bigger Network Data … Dr. Kim K. Larsen / Big Data @ NT 5 ~2.5 IoT connections per Household ~13 IoT connections per Household ~300+ IoT connections per km2 urban area. ~1700+ IoT connections per km2 urban area Frankfurt City has ca. 3,000 pop per km2 Germany2024Expect 250– 500Million IoT Connections Up-to 300+billion Extra events per day
  6. 6. A Network-Centric View. Dr. Kim K. Larsen / Big Data @ NT 6 € User Experience (in Network) Network Incidents Network Optimization
  7. 7. The Functional Scope. Focuses on main strategic directions from NT Perspective. Dr. Kim K. Larsen / Big Data @ NT 7 • Network Enrichment of data-driven business models & decisions. • Enables 360o user experience management. • External monetization possibilities (e.g., B2B, location, adverts, credit rating, security, etc…). Data Driven Business  Anomaly detection.  Events & Incidents.  Self-restoration.  Self-Healing.  Security.  “Zero-touch” Operations. Network Operations Minutes→Milliseconds  Utilization management.  Self-optimization.  Self-configuring.  Resource management.  Congestion management.  Zero-touch” Optimization. Network Optimization Month→Week→Milliseconds  Reporting, KPIs, ….  Data enrichment/augmentation.  Classical (re-active) CEM.  NG (pro-active) CEM (NRT).  AI-driven customer care.  “Zero-touch” UX. User Experience Reactive→Proactive
  8. 8. Plan ahead for Big Data. Avoid the usual suspects … Dr. Kim K. Larsen / Big Data @ NT 8 Use case 1 Reqs x,y,z,.. Use case 2 Reqs a,b,z,.. Use case 3 Reqs a,k,p,.. Use case N Reqs x,y,q,.. …. Harmonized Architectural ConceptUse Case 1 Design Use Case 2 Design Use Case 3 Design … Design RT Design Near-RT Design Non-RT We have to deal with a (large) number of use cases. Go for a Harmonized Architectural Concept! Avoid Ad-hoc Single Use Case driven system design.
  9. 9. Big Network. The network context and its relation to Big Data and ML. Dr. Kim K. Larsen / Big Data @ NT 9 Telco Network Machine Learning Apps Big Data Analytics
  10. 10. What is your Real-Time time-scale? Dr. Kim K. Larsen / Big Data @ NT 10 Merriam-Webster: “Real-Time is the actual time during which something takes place.”
  11. 11. Telco Real-Time Domain. Dr. Kim K. Larsen / Big Data @ NT 11 time scale ~50ms ~500ms~5ms minutes hours daysµs RT Telco World New territory  Most Real-Time demands ranges from 5 ms up-to 500 ms.  The wide range is covered by different Big Data technologies.  “Tactile domain” drives new uses cases asking for 1ms and lower. Near-Real TimeReal-Time Tactile domain Non-Real Time
  12. 12. The Meaning of “Right-in-Time” Dr. Kim K. Larsen / Big Data @ NT 12  Use case dependent time-scale.  Reaction times; µs, ms, sec up to min or even hours.  E.g. if relevant time is hours, no need to analyze in millisecond. time scale ~50ms ~500ms~5ms minutes hours daysµs SQM / CEM Status reporting “Tactile” apps Network optimization Fault detection Incident mgmt RT Telco World Marketing related data analytics streaming micro batch processing batch and backend processing new territory
  13. 13. Right-in-Time Network Architecture. Converged network vision. Dr. Kim K. Larsen / Big Data @ NT 13 Right-in-TimeBigData Virtualized Network and Service functions Infrastructure Cloud NG IP Network (BNG/TeraStream) Mobile Access Fixed Access CPESIM Hybrid Virtualized Network and Service functions Infrastructure Cloud NG IP Network (BNG/TeraStream) Mobile Access Fixed Access CPESIM Hybrid Real-TimeNetwork& ServiceManager
  14. 14. Challenges ... The Next Steps. ML in the Real-Time Domain … from seconds to milliseconds. Dr. Kim K. Larsen / Big Data @ NT 14 Data Sources (Data Generation Entity) Data Stream { X(t) } Process (e.g., filter, route, enrich, compute) Transport Decision Point (e.g., ML model) Data Stream { X(t), F(X(t)) } Transport Store (e.g., HDFS) Store or in-memory Change Order Input Output t0 t1 Roundtrip time Scale ~ms t2 Batch Process Typical timescales from  ms and up Insights Typical timescales Minutes  Daily  Monthly + Ad-hoc Streaming or micro-batch processing MachineLearning Apps
  15. 15. Danger of Over-Engineering Solutions. Dr. Kim K. Larsen / Big Data @ NT 15 Very efficientsolution! GoodBike Very expensive& complexsolution! Bad“Bike” vs A B Best Solution? Desired outcomeNeed or Desire e.g., GLM, Kernels, or parsing e.g., DCNN, RNN, … Which one of below solutions are the best bike solution?
  16. 16. The Entanglement Challenge. Many machine learning agents (or apps) with different objectives will be present in a modern control system. Machine Learning App “Machine Learning Systems mix signals together, entangling them & makes isolation of improvements largely impossible & stability at risk.” (RTx) SON AI (RTy) CEM AI Simple illustration Optimize cell for best cell performance Optimize cell (& terminal?) for best user experience Reference: D, Sculley et al (2015), “Hidden Technical Debts in Machine Learning”. ? Dr. Kim K. Larsen / Big Data @ NT
  17. 17. Simple Agents Interacts in Very Complex Ways! Dr. Kim K. Larsen / Big Data @ NT 17 “Bots reverted another bot’s change on average 105 times, significantly larger than the average of 3 times for humans”. Source: Tsvetkova et al., “Even Good Bots Fight, https://arxiv.org/ftp/arxiv/papers/1609/1609.04285.pdf Bot-Bot interactions on Wikipedia Human-Human interactions on Wikipedia “Bots intended to support often undo each other’s changes and these “fights” may sometimes continue for years”. “Research suggests that even relatively “dumb” bots may give rise to complex interactions.”
  18. 18. 18 Does it work? No Yes Fail Fast Fail Often Rapid proto-typing & proof-of-concepts. Architecture is about building stuff. Dr. Kim K. Larsen / Big Data @ NT
  19. 19. Big Data … Core Technology Beliefs. Non-exhaustive, i.e., just a subset. Dr. Kim K. Larsen / Big Data @ NT 19 We (DT) own the data. 1 Harmonization more important than Centralization. 2 RT and Non-RT co-exist, both need to be embraced in a “Right in Time” concept. 4 “Right in Time” implies that a single technology does not solve every Big Data challenge. 5 Benefits from shared local Big Data lake substantial. 3
  20. 20. Next Developing Steps. Dr.KimK.Larsen/BigData @NT 20 Developing a Big Data Architecture in the Tactile Domain Study Real Time (e.g., ms – sec domain) requirements. Study System Engineering requirements for Tactile Applications. Develop proof of concepts – Fail fast philosophy! Developing RT Applied Machine Learning expertise Feasibility study of Deep Learning Algorithms applied to RT. Applied Machine Learning in Tactile Domain, e.g., dynamic algorithms. Alternatives: Genetic algorithms, scale-free networks. Developing re-enforcement learning applications. Spectrum auctions, network management, customer experience, self- optimized network applications, etc..
  21. 21. Dr.KimK.Larsen/BigData @NT 21 Acknowledgement Wolfgang Wölker and many other colleagues who have contributed with valuable insights & comments throughout this work.

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