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Get Cloud Resources to the IoT Edge with Fog Computing
Get	
  Cloud	
  Resources	
  to	
  the	
  IoT	
  
Edge	
  with	
  Fog	
  Compu8ng	
  
Biren	
  Gandhi	
  
Principal	
  Strategist	
  
Corporate	
  Strategic	
  Innova8on	
  Group,	
  Cisco	
  
	
  
	
  
@birengandhi	
   /in/birengandhi	
  
Abstract:	
  
	
  
Embedded	
  devices	
  in	
  the	
  IoT	
  are	
  hampered	
  by	
  the	
  compute,	
  storage,	
  and	
  service	
  
limita8ons	
  of	
  living	
  life	
  on	
  the	
  edge.	
  As	
  IoT	
  edge	
  devices	
  comprise	
  broader	
  sensor	
  networks	
  
for	
  industrial	
  automa8on,	
  transporta8on,	
  and	
  other	
  safety	
  cri8cal	
  applica8ons,	
  their	
  high	
  
up8me	
  requirements	
  are	
  nonnego8able	
  and	
  service	
  latencies	
  must	
  be	
  kept	
  within	
  real-­‐8me	
  
or	
  near	
  real-­‐8me	
  parameters.	
  However,	
  the	
  size,	
  weight,	
  power,	
  and	
  cost	
  constraints	
  of	
  
edge	
  plaLorms	
  also	
  inhibit	
  the	
  on	
  device	
  resources	
  available	
  for	
  execu8ng	
  such	
  func8ons.	
  
In	
  this	
  session,	
  Gandhi	
  will	
  introduce	
  Fog	
  Compu8ng,	
  a	
  new	
  paradigm	
  for	
  the	
  IoT	
  that	
  
extends	
  compute,	
  storage,	
  and	
  applica8on	
  resources	
  from	
  the	
  cloud	
  to	
  the	
  network	
  edge.	
  
Beyond	
  the	
  interplay	
  between	
  Fog	
  and	
  Cloud,	
  Gandhi	
  will	
  show	
  how	
  Fog	
  services	
  can	
  be	
  
leveraged	
  across	
  a	
  range	
  of	
  heterogeneous	
  plaLorms—from	
  end	
  user	
  devices	
  and	
  access	
  
points	
  to	
  edge	
  routers	
  and	
  switches—through	
  soOware	
  technology	
  that	
  facilitates	
  the	
  
collec8on,	
  storage,	
  analysis,	
  and	
  fusion	
  of	
  data	
  to	
  drive	
  success	
  in	
  your	
  next	
  IoT	
  device	
  
deployment.	
  
	
  
Simplified	
  Abstract:	
  
What	
  is	
  Fog	
  Compu8ng	
  and	
  how	
  can	
  it	
  solve	
  IoT	
  pain	
  points?	
  
IoT	
  (and	
  IoE)	
  
Delivering	
  the	
  Right	
  
Informa8on	
  to	
  the	
  Right	
  
Person	
  (or	
  Machine)	
  	
  
at	
  the	
  Right	
  Time	
  	
  
Process	
  
Physical	
  Devices	
  and	
  
Objects	
  Connected	
  to	
  the	
  
Internet	
  and	
  Each	
  Other	
  
for	
  Intelligent	
  Decision	
  
Making	
  
Things	
  
Connec8ng	
  People	
  in	
  	
  
More	
  Relevant,	
  	
  
Valuable	
  Ways	
  
People	
  
Leveraging	
  Data	
  into	
  
More	
  Useful	
  
Informa8on	
  for	
  
Decision	
  Making	
  	
  	
  
Data	
  
IoE	
  
Networked	
  Connec8on	
  of	
  People,	
  Process,	
  
Data,	
  Things	
  
IoT	
  is	
  Here	
  –	
  Now	
  and	
  Growing	
  
TIMELINE	
  
2010	
   2015	
   2020	
  
BILLIONS	
  OF	
  DEVICES	
  	
  
0	
  
10	
  
20	
  
30	
  
50	
  
Adop?on	
  rate	
  of	
  digital	
  
infrastructure:	
  
5X	
  faster	
  than	
  electricity	
  	
  
and	
  telephony	
  
25	
  
12.5	
  
7.2	
  6.8	
   7.6	
  
World	
  Popula?on	
  
50	
   Billion	
  	
  
Smart	
  Objects	
  
Inflec?on	
  
point	
  
IoT	
  Ver8cals	
  in	
  Ac8on	
  
Source:	
  Cisco	
  IoT	
  Purchase	
  Process	
  Global	
  Study	
  January	
  2015,	
  N=	
  2582	
  
	
  
MANUFACTURING	
  
Inventory	
  
Management	
  
Real-­‐8me	
  Monitoring	
  
Energy	
  Management	
  
GOVT	
  
TheO	
  Protec8on	
  
Asset	
  tracking	
  
Real-­‐8me	
  Billing	
  
UTILITIES	
  
Fleet	
  Management	
  
Real-­‐8me	
  Asset	
  
Tracking	
  
Safety	
  and	
  
Compliance	
  
TRANSPORTATION	
  
Fleet	
  Management	
  
Inventory	
  
Management	
  
Op8mize	
  delivery	
  	
  
RETAIL	
  
TheO	
  Protec8on	
  
Inventory	
  
Management	
  
Real-­‐8me	
  Billing	
  
Delivering	
  Outcomes	
  -­‐	
  NOW	
  
MANUFACTURING	
  
Safety	
  
Product	
  Quality	
  
Supply	
  Cain	
  and	
  
Logis8cs	
  
GOVT	
  
Safety	
  
Efficiency	
  	
  
Customer	
  Sa8sfac8on	
  
UTILITIES	
  
Safety	
  
Customer	
  Sa8sfac8on	
  
Supply	
  Chain	
  and	
  
Logis8cs	
  
TRANSPORTATION	
  
Safety	
  
Customer	
  Sa8sfac8on	
  
Supply	
  Chain	
  and	
  
Logis8cs	
  
RETAIL	
  
Safety	
  
Customer	
  Sa8sfac8on	
  
Cost	
  Reduc8on	
  	
  
Source:	
  Cisco	
  IoT	
  Purchase	
  Process	
  Global	
  Study	
  January	
  2015,	
  N=	
  2582	
  
	
  
Accelera8ng	
  IoT	
  Deployment	
  	
  
And	
  So	
  Do	
  Challenges	
  …	
  
Many	
  of	
  these	
  are	
  technology	
  challenges	
  
Key	
  IoT	
  Challenges	
  (1)	
  
•  Data	
  Size/Sources	
  (Velocity,	
  Variety,	
  Volume)	
  
– Geo-­‐distribu8on	
  
– M2M	
  Chamer	
  (status,	
  health,	
  etc.)	
  
•  IT	
  meets	
  OT	
  
– Local	
  Control	
  Loops	
  (determinis8c	
  behavior)	
  
– Cyber	
  Physical	
  Threats/Security	
  
•  System-­‐wide	
  View	
  
– e.g.	
  Smart	
  Traffic	
  Light	
  System	
  	
  
Key	
  IoT	
  Challenges	
  (2)	
  
•  Cohesive	
  Opera8ons	
  
– Resource	
  Orchestra8on	
  &	
  Monitoring	
  
– Distributed	
  Policy	
  Management	
  
– Joint/Delegated/Contractual	
  Ownership	
  	
  
•  Data	
  Processing	
  &	
  Analy8cs	
  
– Balance	
  among	
  Real-­‐8me,	
  Semi-­‐real-­‐8me	
  and	
  
Non-­‐real-­‐8me	
  
– Aggrega8on	
  and	
  Bandwidth/Compute	
  Cost	
  
Key	
  IoT	
  Challenges	
  (3)	
  
•  Reliability/Availability	
  
–  Unlike	
  DC,	
  “failure”	
  is	
  a	
  norm	
  	
  
•  Complex	
  Greenfield/Brownfield	
  Deployment	
  
–  Longer	
  Life	
  of	
  Cri8cal	
  Infrastructural	
  Systems	
  
–  Huge	
  Deployment/Opera8onal	
  Costs	
  	
  
•  Interplay	
  with	
  the	
  Cloud	
  	
  
–  Many	
  Silo’ed	
  “PlaLorms”	
  (per	
  vendor)	
  
–  Detached	
  Apps	
  (driven	
  from	
  consumer	
  domain)	
  
–  Ecosystem	
  of	
  Tools	
  and	
  Prac8ces	
  	
  
In	
  Summary	
  
IoT	
  provides	
  huge,	
  
disrup8ve	
  
opportuni8es	
  
IoT	
  is	
  rapidly	
  adopted	
  
at	
  an	
  accelerated	
  pace	
  
IoT	
  demands	
  new	
  
architectural	
  
paradigm	
  to	
  deal	
  with	
  
unique	
  challenges	
  
Meet	
  “Fog	
  Compu8ng”	
  
	
  
“A	
  distributed	
  compu8ng	
  paradigm	
  that	
  	
  
extends	
  cloud	
  compu.ng	
  
closer	
  to	
  the	
  edge	
  of	
  the	
  network	
  
to	
  enable	
  new	
  wave	
  of	
  applica.ons	
  and	
  services	
  
in	
  IoT	
  land”	
  
	
  
Example:	
  Smart	
  Traffic	
  Light	
  System	
  
•  Traffic	
  Light	
  at	
  Intersec8on	
  
–  Goal:	
  accident	
  preven.on	
  
–  Fair	
  Traffic	
  Management	
  	
  
–  Detects	
  vehicles/
pedestrians/cyclist	
  crossing	
  
–  Measures	
  distance	
  &	
  
speed	
  of	
  approaching	
  
vehicles	
  
–  Issues	
  alarms	
  to	
  
approaching	
  vehicles	
  
–  Changes	
  from	
  Green	
  to	
  
Red	
  
–  Takes	
  photos	
  &	
  issues	
  
8cket	
  
•  STLS	
  as	
  a	
  system	
  
–  Goal:	
  facilitate	
  smooth	
  
traffic	
  flow	
  throughout	
  the	
  
city/region	
  
–  Traffic	
  conges8on	
  
measurements	
  
–  Route	
  recommenda8on	
  
(no	
  loops,	
  please!)	
  
–  Coordina8on	
  of	
  cycles	
  of	
  
individual	
  intersec8on	
  
–  Emergency	
  Evacua8on	
  
Assistance	
  
–  Crime-­‐in-­‐progress	
  
Detec8on	
  
Simplified	
  Requirements	
  
•  Local	
  subsystem	
  (traffic	
  light,	
  sensors,	
  actuators)	
  
-­‐  Reac8on	
  8me	
  <	
  10	
  msec	
  
-­‐  Compute/storage	
  capability	
  
-­‐  Ruggedized	
  &	
  small	
  form	
  factor	
  box	
  
•  Global	
  system	
  
-­‐  Wide	
  geo-­‐distribu8on	
  
-­‐  Middleware	
  orchestra8on	
  
-­‐  Mul8plicity	
  of	
  agencies	
  running	
  the	
  system	
  (must	
  coordinate	
  
control	
  policies)	
  
•  Interac8on	
  with	
  Cloud	
  
–  Efficient	
  traffic	
  management	
  demands	
  large	
  database	
  of	
  
historical	
  records	
  w/	
  heuris8cs/analy8cs	
  capabili8es	
  	
  
This	
  is	
  Fog	
  
Compu8ng!	
  	
  
(cloud	
  –	
  but	
  closer	
  to	
  
the	
  ground)	
  	
  
What	
  Exactly	
  is	
  Fog?	
  
INTERNET	
  OF	
  THINGS	
  
APPLICATIONS	
  
Cloud	
  
Fog	
  
Cloud	
  capabili8es	
  
extended	
  closer	
  
to	
  the	
  edge	
  
Consumer	
  domain’s	
  app-­‐
centric	
  model	
  of	
  device-­‐to-­‐
cloud	
  may	
  not	
  be	
  enough	
  	
  
Hierarchical	
  Fog	
  Architecture	
  
Cloud	
  
Device/Smart	
  Object	
  
East/West	
  Flows	
  Fog	
  
Fog Nodes can be multi-tenant
Shared, public or private (like cloud)
Highly virtualized environment
Secured & isolated tenants, QoS, workload distribution
Service Mobility
Ability to migrate a running instance from cloud to edge
North/South	
  Flows	
  
Mixed ownership & operation
Single entity, federation of agencies
Fog	
  Compu8ng	
  PlaLorm	
  
•  Enables	
  low-­‐latency,	
  near-­‐real-­‐8me	
  compute	
  
capabili8es	
  closer	
  to	
  the	
  edge	
  
•  Tames	
  data	
  deluge	
  and	
  effec8vely	
  u8lizes	
  costly	
  
bandwidth	
  
•  Leverages	
  proven	
  cloud	
  eco-­‐system	
  of	
  tools,	
  
technologies,	
  best	
  prac8ces	
  and	
  developer	
  API	
  to	
  
support	
  mul8-­‐agency	
  orchestra8on	
  and	
  management	
  
	
  
In	
  short,	
  think	
  of	
  it	
  as	
  a	
  Sophis8cated	
  App	
  Store	
  model	
  
for	
  heterogeneous	
  IoT	
  infrastructure	
  	
  
Fog	
  Compu8ng	
  Example	
  Use	
  Cases	
  
Smart Traffic Lights
Real-time (RT) local control loop
Geo-distributed orchestration
Multiagency policy co-ordination
Local/Global Analytics
L	
  G	
   C	
   O	
  
Wind Farm
RT local control loop
In-situ orchestration
Global Big Data
L	
  G	
   C	
  
Connected Rail
Two-tier wireless AP
Fast mobility
Low latency streaming
RT actionable analytics
Global big data
M	
   L	
  G	
   C	
   O	
  
Retailing
Video analytics
Interplay between local and
Globally processed data
L	
   C	
  
Oil & Gas
RT actionable analytics
Geo-distributed Orchestration
Industrial automation, Big data
M	
   L	
  G	
   C	
  
SCV & Transport
RT actionable analytics
Global Big Data
(batch processing)
M	
   L	
   C	
  
Military Apps
Real-time local control loop
Geo-distributed Orchestration
Multiagency policy co-ordination
Local/Global Analytics
M	
   L	
  G	
   C	
   O	
  
M	
   L	
  G	
   C	
   O	
  Mobility	
   Geo-­‐distribu8on	
   Low/predictable	
  latency	
   Cloud	
  interac8on	
   Mul8-­‐agent	
  orchestra8on	
  
Cri8cal	
  amributes	
  
Data	
  is	
  the	
  new	
  $$$	
  
1.1 Billion
Data points generated by sensors daily
500 Gigabytes
Data generated by an offshore oil rig weekly
1000 Gigabytes
Data generated by an oil refinery daily
10,000 Gigabytes
Data generated by a jet engine every 30 minutes
2.5 Billion Gigabytes
Data generated worldwide daily
90% of the world’s data has been created in the last 2 years!
IoT	
  Brings	
  New	
  Data	
  Dimensions	
  
Data	
  Interplay	
  between	
  Fog	
  &	
  Cloud	
  
FogSensors Cloud	
  
Storage
Realtime Data
Control/Actuation
Transient
milliSec/Seconds
Visualisation
_-­‐-­‐_-­‐	
   Analytics
Seconds/Minutes
Semi-Permanent Months/Years
Minutes/Days/Weeks
Filter to process data locally
Remaining pass to cloud
Response
_-­‐-­‐_-­‐	
   Business Intelligence
Dashboards
KPIs
_-­‐-­‐_-­‐	
  
_-­‐-­‐_-­‐	
  
Control
Loop
In	
  8me	
  and	
  in	
  space	
  
Localization Globalization
Data	
  Processing	
  at	
  the	
  Edge	
  
•  Need	
  of	
  immediate	
  response	
  8me	
  
–  Can't	
  afford	
  latency	
  of	
  sending	
  up	
  and	
  back	
  the	
  chain	
  
•  Closed-­‐loop	
  control	
  
–  In	
  controlling	
  physical	
  systems	
  –	
  cannot	
  depend	
  on	
  speed	
  and	
  availability	
  of	
  resources	
  
back	
  at	
  the	
  data	
  center	
  –	
  e.g.	
  smart	
  traffic	
  light	
  system	
  
•  Privacy,	
  Security	
  &	
  Data-­‐ownership	
  considera8ons	
  
–  Regulatory	
  and	
  business	
  concerns	
  may	
  not	
  allow	
  moving	
  the	
  data	
  
•  Improved	
  scale	
  and	
  aggregate	
  throughput	
  via	
  parallelism	
  
–  Data	
  sources	
  oOen	
  naturally	
  distributed	
  
•  Not	
  all	
  Bytes	
  are	
  born	
  equal	
  
–  Offload	
  centralized	
  resources	
  that	
  would	
  otherwise	
  have	
  to	
  filter	
  through	
  volumes	
  of	
  
uninteres8ng/useless	
  data.	
  
Fog	
  Compu8ng	
  –	
  Buzz	
  or	
  Business?	
  
Let’s	
  check	
  out	
  some	
  use	
  cases	
  
Railway	
  Systems	
  
Immediate	
  Response	
  	
  
to	
  Equipment	
  Failure	
  
Real-­‐8me	
  Health	
  Status	
  of	
  
Trains	
  &	
  Tracks	
  
New	
  Passenger	
  Ameni8es	
  
and	
  Services	
  REPLACE	
  
BEARINGS	
  
CAR	
  	
  07	
  
Oil	
  Pipelines	
  
Proac8ve	
  Leak	
  Detec8on	
  
Predic8ve	
  Management	
  
Broadband	
  Connec8on	
  for	
  
Community	
  
S6 | C026 Pressure	
  
Drop	
  
	
  
kPa	
  
ACTION	
  REQUIRED	
  
U8lity	
  Substa8on	
  
Local	
  Load	
  Balancing	
  
Opera8onal	
  Efficiencies	
  
New	
  Business	
  Models	
  
01 02 03 04 05 06 07 08 09 10
Maintenance	
  
Needed	
  on	
  
Transformer	
  7	
  
Manufacturing	
  
Predic8ve	
  Maintenance	
  
Op8mize	
  Energy	
  Usage	
  
Security	
  Before,	
  During,	
  
and	
  AOer	
  Amack	
  
QUALITY	
   POWER	
   STAMPS	
  
35%	
   85%	
   592547/	
  
1000000	
  
City	
  Infrastructure	
  
Synchronize	
  Signals	
  for	
  
Emergency	
  Vehicles	
  
Improve	
  Conges8on	
  
Management	
  
Bemer	
  Profitability	
  
Fog	
  Compu8ng	
  @	
  Cisco	
  Live	
  
Video	
  of	
  Fog	
  Compu8ng	
  demo	
  	
  
at	
  Cisco	
  Live	
  San	
  Diego	
  
Cisco	
  IoT	
  System	
  
INTERNET	
  OF	
  THINGS	
  
APPLICATIONS	
  
Fog	
  	
  
Compu?ng	
  
Management	
  	
  
and	
  Automa?on	
  
Network	
  
Connec?vity	
  
Security	
  
Cyber	
  and	
  Physical	
  
Data	
  	
  
Analy?cs	
  
Applica?on	
  	
  
Enablement	
  
PlaWorm	
  
Cloud	
  Fog	
  
Ques8ons?	
  
Fog	
  
Thanks	
  
Learn	
  more	
  about	
  Cisco	
  IoT	
  System:	
  
hmp://bit.ly/IoTSystem	
  
	
  
	
  
@birengandhi	
   /in/birengandhi	
  

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Get Cloud Resources to the IoT Edge with Fog Computing

  • 2. Get  Cloud  Resources  to  the  IoT   Edge  with  Fog  Compu8ng   Biren  Gandhi   Principal  Strategist   Corporate  Strategic  Innova8on  Group,  Cisco       @birengandhi   /in/birengandhi  
  • 3. Abstract:     Embedded  devices  in  the  IoT  are  hampered  by  the  compute,  storage,  and  service   limita8ons  of  living  life  on  the  edge.  As  IoT  edge  devices  comprise  broader  sensor  networks   for  industrial  automa8on,  transporta8on,  and  other  safety  cri8cal  applica8ons,  their  high   up8me  requirements  are  nonnego8able  and  service  latencies  must  be  kept  within  real-­‐8me   or  near  real-­‐8me  parameters.  However,  the  size,  weight,  power,  and  cost  constraints  of   edge  plaLorms  also  inhibit  the  on  device  resources  available  for  execu8ng  such  func8ons.   In  this  session,  Gandhi  will  introduce  Fog  Compu8ng,  a  new  paradigm  for  the  IoT  that   extends  compute,  storage,  and  applica8on  resources  from  the  cloud  to  the  network  edge.   Beyond  the  interplay  between  Fog  and  Cloud,  Gandhi  will  show  how  Fog  services  can  be   leveraged  across  a  range  of  heterogeneous  plaLorms—from  end  user  devices  and  access   points  to  edge  routers  and  switches—through  soOware  technology  that  facilitates  the   collec8on,  storage,  analysis,  and  fusion  of  data  to  drive  success  in  your  next  IoT  device   deployment.     Simplified  Abstract:   What  is  Fog  Compu8ng  and  how  can  it  solve  IoT  pain  points?  
  • 4. IoT  (and  IoE)   Delivering  the  Right   Informa8on  to  the  Right   Person  (or  Machine)     at  the  Right  Time     Process   Physical  Devices  and   Objects  Connected  to  the   Internet  and  Each  Other   for  Intelligent  Decision   Making   Things   Connec8ng  People  in     More  Relevant,     Valuable  Ways   People   Leveraging  Data  into   More  Useful   Informa8on  for   Decision  Making       Data   IoE   Networked  Connec8on  of  People,  Process,   Data,  Things  
  • 5. IoT  is  Here  –  Now  and  Growing   TIMELINE   2010   2015   2020   BILLIONS  OF  DEVICES     0   10   20   30   50   Adop?on  rate  of  digital   infrastructure:   5X  faster  than  electricity     and  telephony   25   12.5   7.2  6.8   7.6   World  Popula?on   50   Billion     Smart  Objects   Inflec?on   point  
  • 6. IoT  Ver8cals  in  Ac8on   Source:  Cisco  IoT  Purchase  Process  Global  Study  January  2015,  N=  2582     MANUFACTURING   Inventory   Management   Real-­‐8me  Monitoring   Energy  Management   GOVT   TheO  Protec8on   Asset  tracking   Real-­‐8me  Billing   UTILITIES   Fleet  Management   Real-­‐8me  Asset   Tracking   Safety  and   Compliance   TRANSPORTATION   Fleet  Management   Inventory   Management   Op8mize  delivery     RETAIL   TheO  Protec8on   Inventory   Management   Real-­‐8me  Billing  
  • 7. Delivering  Outcomes  -­‐  NOW   MANUFACTURING   Safety   Product  Quality   Supply  Cain  and   Logis8cs   GOVT   Safety   Efficiency     Customer  Sa8sfac8on   UTILITIES   Safety   Customer  Sa8sfac8on   Supply  Chain  and   Logis8cs   TRANSPORTATION   Safety   Customer  Sa8sfac8on   Supply  Chain  and   Logis8cs   RETAIL   Safety   Customer  Sa8sfac8on   Cost  Reduc8on     Source:  Cisco  IoT  Purchase  Process  Global  Study  January  2015,  N=  2582    
  • 9. And  So  Do  Challenges  …   Many  of  these  are  technology  challenges  
  • 10. Key  IoT  Challenges  (1)   •  Data  Size/Sources  (Velocity,  Variety,  Volume)   – Geo-­‐distribu8on   – M2M  Chamer  (status,  health,  etc.)   •  IT  meets  OT   – Local  Control  Loops  (determinis8c  behavior)   – Cyber  Physical  Threats/Security   •  System-­‐wide  View   – e.g.  Smart  Traffic  Light  System    
  • 11. Key  IoT  Challenges  (2)   •  Cohesive  Opera8ons   – Resource  Orchestra8on  &  Monitoring   – Distributed  Policy  Management   – Joint/Delegated/Contractual  Ownership     •  Data  Processing  &  Analy8cs   – Balance  among  Real-­‐8me,  Semi-­‐real-­‐8me  and   Non-­‐real-­‐8me   – Aggrega8on  and  Bandwidth/Compute  Cost  
  • 12. Key  IoT  Challenges  (3)   •  Reliability/Availability   –  Unlike  DC,  “failure”  is  a  norm     •  Complex  Greenfield/Brownfield  Deployment   –  Longer  Life  of  Cri8cal  Infrastructural  Systems   –  Huge  Deployment/Opera8onal  Costs     •  Interplay  with  the  Cloud     –  Many  Silo’ed  “PlaLorms”  (per  vendor)   –  Detached  Apps  (driven  from  consumer  domain)   –  Ecosystem  of  Tools  and  Prac8ces    
  • 13. In  Summary   IoT  provides  huge,   disrup8ve   opportuni8es   IoT  is  rapidly  adopted   at  an  accelerated  pace   IoT  demands  new   architectural   paradigm  to  deal  with   unique  challenges  
  • 14. Meet  “Fog  Compu8ng”     “A  distributed  compu8ng  paradigm  that     extends  cloud  compu.ng   closer  to  the  edge  of  the  network   to  enable  new  wave  of  applica.ons  and  services   in  IoT  land”    
  • 15. Example:  Smart  Traffic  Light  System   •  Traffic  Light  at  Intersec8on   –  Goal:  accident  preven.on   –  Fair  Traffic  Management     –  Detects  vehicles/ pedestrians/cyclist  crossing   –  Measures  distance  &   speed  of  approaching   vehicles   –  Issues  alarms  to   approaching  vehicles   –  Changes  from  Green  to   Red   –  Takes  photos  &  issues   8cket   •  STLS  as  a  system   –  Goal:  facilitate  smooth   traffic  flow  throughout  the   city/region   –  Traffic  conges8on   measurements   –  Route  recommenda8on   (no  loops,  please!)   –  Coordina8on  of  cycles  of   individual  intersec8on   –  Emergency  Evacua8on   Assistance   –  Crime-­‐in-­‐progress   Detec8on  
  • 16. Simplified  Requirements   •  Local  subsystem  (traffic  light,  sensors,  actuators)   -­‐  Reac8on  8me  <  10  msec   -­‐  Compute/storage  capability   -­‐  Ruggedized  &  small  form  factor  box   •  Global  system   -­‐  Wide  geo-­‐distribu8on   -­‐  Middleware  orchestra8on   -­‐  Mul8plicity  of  agencies  running  the  system  (must  coordinate   control  policies)   •  Interac8on  with  Cloud   –  Efficient  traffic  management  demands  large  database  of   historical  records  w/  heuris8cs/analy8cs  capabili8es     This  is  Fog   Compu8ng!     (cloud  –  but  closer  to   the  ground)    
  • 17. What  Exactly  is  Fog?   INTERNET  OF  THINGS   APPLICATIONS   Cloud   Fog   Cloud  capabili8es   extended  closer   to  the  edge   Consumer  domain’s  app-­‐ centric  model  of  device-­‐to-­‐ cloud  may  not  be  enough    
  • 18. Hierarchical  Fog  Architecture   Cloud   Device/Smart  Object   East/West  Flows  Fog   Fog Nodes can be multi-tenant Shared, public or private (like cloud) Highly virtualized environment Secured & isolated tenants, QoS, workload distribution Service Mobility Ability to migrate a running instance from cloud to edge North/South  Flows   Mixed ownership & operation Single entity, federation of agencies
  • 19. Fog  Compu8ng  PlaLorm   •  Enables  low-­‐latency,  near-­‐real-­‐8me  compute   capabili8es  closer  to  the  edge   •  Tames  data  deluge  and  effec8vely  u8lizes  costly   bandwidth   •  Leverages  proven  cloud  eco-­‐system  of  tools,   technologies,  best  prac8ces  and  developer  API  to   support  mul8-­‐agency  orchestra8on  and  management     In  short,  think  of  it  as  a  Sophis8cated  App  Store  model   for  heterogeneous  IoT  infrastructure    
  • 20. Fog  Compu8ng  Example  Use  Cases   Smart Traffic Lights Real-time (RT) local control loop Geo-distributed orchestration Multiagency policy co-ordination Local/Global Analytics L  G   C   O   Wind Farm RT local control loop In-situ orchestration Global Big Data L  G   C   Connected Rail Two-tier wireless AP Fast mobility Low latency streaming RT actionable analytics Global big data M   L  G   C   O   Retailing Video analytics Interplay between local and Globally processed data L   C   Oil & Gas RT actionable analytics Geo-distributed Orchestration Industrial automation, Big data M   L  G   C   SCV & Transport RT actionable analytics Global Big Data (batch processing) M   L   C   Military Apps Real-time local control loop Geo-distributed Orchestration Multiagency policy co-ordination Local/Global Analytics M   L  G   C   O   M   L  G   C   O  Mobility   Geo-­‐distribu8on   Low/predictable  latency   Cloud  interac8on   Mul8-­‐agent  orchestra8on   Cri8cal  amributes  
  • 21. Data  is  the  new  $$$   1.1 Billion Data points generated by sensors daily 500 Gigabytes Data generated by an offshore oil rig weekly 1000 Gigabytes Data generated by an oil refinery daily 10,000 Gigabytes Data generated by a jet engine every 30 minutes 2.5 Billion Gigabytes Data generated worldwide daily 90% of the world’s data has been created in the last 2 years! IoT  Brings  New  Data  Dimensions  
  • 22. Data  Interplay  between  Fog  &  Cloud   FogSensors Cloud   Storage Realtime Data Control/Actuation Transient milliSec/Seconds Visualisation _-­‐-­‐_-­‐   Analytics Seconds/Minutes Semi-Permanent Months/Years Minutes/Days/Weeks Filter to process data locally Remaining pass to cloud Response _-­‐-­‐_-­‐   Business Intelligence Dashboards KPIs _-­‐-­‐_-­‐   _-­‐-­‐_-­‐   Control Loop In  8me  and  in  space   Localization Globalization
  • 23. Data  Processing  at  the  Edge   •  Need  of  immediate  response  8me   –  Can't  afford  latency  of  sending  up  and  back  the  chain   •  Closed-­‐loop  control   –  In  controlling  physical  systems  –  cannot  depend  on  speed  and  availability  of  resources   back  at  the  data  center  –  e.g.  smart  traffic  light  system   •  Privacy,  Security  &  Data-­‐ownership  considera8ons   –  Regulatory  and  business  concerns  may  not  allow  moving  the  data   •  Improved  scale  and  aggregate  throughput  via  parallelism   –  Data  sources  oOen  naturally  distributed   •  Not  all  Bytes  are  born  equal   –  Offload  centralized  resources  that  would  otherwise  have  to  filter  through  volumes  of   uninteres8ng/useless  data.  
  • 24. Fog  Compu8ng  –  Buzz  or  Business?   Let’s  check  out  some  use  cases  
  • 25. Railway  Systems   Immediate  Response     to  Equipment  Failure   Real-­‐8me  Health  Status  of   Trains  &  Tracks   New  Passenger  Ameni8es   and  Services  REPLACE   BEARINGS   CAR    07  
  • 26. Oil  Pipelines   Proac8ve  Leak  Detec8on   Predic8ve  Management   Broadband  Connec8on  for   Community   S6 | C026 Pressure   Drop     kPa   ACTION  REQUIRED  
  • 27. U8lity  Substa8on   Local  Load  Balancing   Opera8onal  Efficiencies   New  Business  Models   01 02 03 04 05 06 07 08 09 10 Maintenance   Needed  on   Transformer  7  
  • 28. Manufacturing   Predic8ve  Maintenance   Op8mize  Energy  Usage   Security  Before,  During,   and  AOer  Amack   QUALITY   POWER   STAMPS   35%   85%   592547/   1000000  
  • 29. City  Infrastructure   Synchronize  Signals  for   Emergency  Vehicles   Improve  Conges8on   Management   Bemer  Profitability  
  • 30. Fog  Compu8ng  @  Cisco  Live   Video  of  Fog  Compu8ng  demo     at  Cisco  Live  San  Diego  
  • 31. Cisco  IoT  System   INTERNET  OF  THINGS   APPLICATIONS   Fog     Compu?ng   Management     and  Automa?on   Network   Connec?vity   Security   Cyber  and  Physical   Data     Analy?cs   Applica?on     Enablement   PlaWorm   Cloud  Fog  
  • 33. Thanks   Learn  more  about  Cisco  IoT  System:   hmp://bit.ly/IoTSystem       @birengandhi   /in/birengandhi