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Arguments for a Unified IoT Architecture

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There is a fuzzy border between the streaming and operational requirements of managing fast data in IoT. There is also an increasingly fuzzy border between where the computation and data management activities should occur. In this webinar, Ryan Betts, CTO of VoltDB highlights the benefits and disadvantages of a unified IoT architecture and discuss how simplifying at the core allows for high throughput, streamlined scalability and data consistency.

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Arguments for a Unified IoT Architecture

  1. 1. page Arguments for a Unified IoT Architecture
  2. 2. page© 2016 VoltDB FUZZY BORDERS 2 Centralized   Edge   Streaming  /  Mechanical   Opera6onal  /  Policy  
  3. 3. page© 2016 VoltDB VENDORS IN TRANSITION 3
  4. 4. page© 2016 VoltDB FAST DATA: GLIMPSE AT INNOVATION 4 OLTP   OLAP   Streaming   VoltDB   NuoDB   Founda6onDB   SQLFire  XD   Amazon  Aurora     MemSQL   Druid   OpenTSDB   TempoIQ   ParStream   InfluxDB   PipelineDB   Storm   Spark  Streaming   Google  Dataflow   Amazon  Kinesis   Tibco  Streambase   IBM  Streams   DataTorrent  
  5. 5. page© 2016 VoltDB APPLICATIONS IN FLUX •  Are  the  "killer  apps  for  IoT"  machine  learning,   fully  automated,  highly  distributed,  driven  by   collec<ve  (centralized)  data,  by  analy<cs,  or   outcomes,  efficiency  or  engagement,  consumer   or  industrial?   5
  6. 6. page© 2016 VoltDB CONTINUUM 6
  7. 7. page© 2016 VoltDB GENERAL SOLUTION 7
  8. 8. page© 2016 VoltDB SIDEBAR: A DEEP STACK Device Security & Policy Communication Fog Data Services More Networks Centralized Compute Data Services Cloud Applications Fast (in motion) Streaming Analytics:     real  'me  summary   aggrega'on,  modeling   Transaction Processing: per-­‐event  decisions  using   context  +  history. Big (at rest) Exploration: data  science,   inves'ga'on  of  large   data  sets   Reporting: recommenda'on   matrixes,  search   indexes,  trend  and  BI  
  9. 9. page© 2016 VoltDB WHAT IS FOG COMPUTING Fog  compu'ng  is  not  a  replacement  for  the   cloud,  but  rather  “a  distributed  compu'ng   paradigm  that  extends  cloud  compu'ng  closer  to   the  edge  of  the  network  to  enable  a  new  wave  of   applica'ons  and  services  in  IoT  land.     hDp://embedded-­‐compu'­‐seeing-­‐through-­‐the-­‐fog-­‐compu'ng/   9
  10. 10. page© 2016 VoltDB RATIONALE: PERFORMANCE REQUIREMENTS Brain v. Octopus 10
  11. 11. page© 2016 VoltDB RATIONALE: DATA VELOCITY AND VOLUME 11 Walmart v. Vending Machine
  12. 12. page© 2016 VoltDB RATIONALE: TIME TO MARKET 12 App Store v. Space Shuttle
  13. 13. page© 2016 VoltDB CLOUD, DATA, IOT VIRTUOUS CYCLE •  IoT is a data driven application •  Data will drive cloud utilization •  IaaS low margin •  Apps drive margin •  Making the data useful drives value •  It’s not just storage. It’s operational •  The more data processed in the front end, the more that will be stored in the cloud CloudData IoT
  14. 14. page© 2016 VoltDB INFORMATION TECHNOLOGY EVOLUTION 1980 1990 2000 2010 Internet - Cloud Client-Server Fast + Big Data – Internet of Things 2020 Internet - Cloud Client-Server Fast + Big Data – Internet of Things 14
  15. 15. page© 2016 VoltDB Fast inbound events requests Application with request/response pattern for context Event initiated, automated action Forwarding of accumulated data to OLAP or Applications Events VoltDB C* Triggered Alerting Hadoop 1 3 2 1 2 3 4 EoD Billing 4 Rule Engine FAST DATA: INGEST, ANALYTICS, TRANSACTIONS
  16. 16. page© 2016 VoltDB Collect Explore AnalyzeAct 16 Big Data analytic results: 1.  Discoveries: seasonal predictions, scientific results, long-term capacity planning 2.  Op'miza'ons:  market segmentation, fraud heuristics, optimal customer journey
  17. 17. page© 2016 VoltDB ACTIVITY AT THE EDGE "...dynamic  distribu'on  of  data  processing  and   analy'cs  with  performance-­‐cri'cal  computa'on   for  applica'ons/data  center  func'ons  at  the   very  edge  of  the  network” 17
  18. 18. page© 2016 VoltDB PATTERNS AND DECISION ENGINES 18 Automated  anomaly   detec'on.  Model  based.     Math  not  business  logic.  
  19. 19. page© 2016 VoltDB CISCO: MOVE TO EDGE TO SCALE PROCESSING 19
  20. 20. page© 2016 VoltDB CHALLENGES TO FOG An  addi'onal  'er  of  compute,  storage  and   networking  resources  distributed  across  many   loca'ons  to  enable  sophis'cated  applica'ons  is   a  complex  and  expensive  undertaking.   20
  21. 21. page© 2016 VoltDB END TO END PRINCIPLE Func'ons  placed  at  low  levels  of  a  system  may  be   redundant  or  of  liDle  value  when  compared  with  the   cost  of  providing  them.  Examples:  bit  error  recovery,   security  using  encryp'on,  duplicate  message   suppression,  recovery  from  system  crashes,  and  delivery   acknowledgement. 21
  22. 22. page© 2016 VoltDB TYRANNY OF SIMPLE SCALE •  HTTP everywhere •  ATM to the desktop •  1000 server clusters vs. engineered systems. 22
  23. 23. page© 2016 VoltDB INDUSTRY DOMINANCE IN CLOUD •  Fog concept driven largely by Cisco, IBM •  Which face industry pressure from cloud builders community-driven-innovation/ pack-the-first-open-hardware-modular-switch/ 23
  24. 24. page© 2016 VoltDB LOOKING FORWARD: THOUGHT EXPERIMENTS •  What’s the “CDN” for IoT – the utility apps are pushed to for scale. Protocol based? Provisioning based? Engineered system? •  What’s the impact of cloud providers expanding geographies of service? What’s the floor on cloud pricing? •  Battery vs. Bandwidth •  Industrial vs. Consumer 24
  26. 26. page© 2016 VoltDB QUESTIONS? •  Use the chat window to type in your questions Ø  Download VoltDB: • •  Open source version is available on •  Check out the Developer Guide to Streaming Data in the resources section on the right side of your screen. •  Join the conversation on Twitter #VoltDBFastData •  All registrants will receive our new eBook – “Architecting for the IoT” via email. 26