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1	
  Intelligent Knowledge-as-a-Service
IoT is a King, Big data is a Queen and
Cloud is a Palace
Abdur Rahim
Innotec21 GmbH, Germany
Create-Net, Italy
Abdur.rahim@create-net.org
Acknowledgements- iKaaS Partners (KDDI and other
partnes)
2	
  
§  Motivation
§  Convergence/opportunities/applications
§  Challenges and requirements
§  Convergence approach
§  iKaaS EU-Japan project
§  Conclusion
Outline
3	
  
Convergence of Technologies
Source-IDC
	
  
	
  
4	
  
Where is the value of IoT?
§  In	
  the	
  past,	
  connec1vity	
  and	
  number	
  of	
  the	
  devices	
  were	
  the	
  
main	
  driver	
  of	
  IoT	
  
§  Data	
  is	
  nothing	
  without	
  big	
  business	
  value	
  insight	
  
§  	
  IoT	
  without	
  BIG	
  DATA	
  is	
  first	
  genera1on	
  of	
  IoT	
  
5	
  
The real value is not just sheer number of
connected devices and data
§  The	
  real	
  opportunity	
  is	
  improved	
  business	
  value-­‐new	
  
revenue	
  models,	
  lower	
  cost,	
  improved	
  client	
  experience,	
  
beGer	
  insight	
  improve	
  outcomes	
  
	
  
Source-IDC
6	
  
Big data-how we understand it
Source:	
  Nenad	
  
7	
  
IoT in BIG data
§  IoT	
  presents	
  challenges	
  in	
  combina1on	
  of	
  all	
  BIG	
  data	
  
characteris1cs	
  (3Vs/4Vs)	
  	
  
§  Most	
  challenging	
  IoT	
  applica1ons	
  match	
  with	
  either	
  or	
  both	
  
Velocity	
  &	
  Volume	
  and	
  some1mes	
  also	
  Variety	
  (situa1on	
  and	
  
context)	
  
§  Velocity	
  driven-­‐applica1on	
  
§  A	
  wearable	
  sensor	
  produces	
  about	
  55	
  million	
  data	
  points	
  pro	
  day	
  
(challenge	
  for	
  storage),	
  whereas	
  some	
  medical	
  wearable's	
  (like	
  
ECG)	
  produce	
  up	
  to	
  1000	
  events	
  per	
  second	
  (challenge	
  for	
  real-­‐
1me	
  processing)	
  
§  Volume	
  driven-­‐applica1ons	
  
§  GE	
  each	
  day	
  gathers	
  50	
  million	
  pieces	
  of	
  data	
  from	
  10	
  million	
  
sensors,	
  off	
  equipment	
  worth	
  $1	
  trillion	
  
	
  
8	
  
Typical IoT applications
Source:	
  Harvad	
  business	
  review	
  	
  
9	
  
IoT BIG data applications
	
  
§  Massive	
  monitoring/Deep	
  understanding	
  (observe	
  of	
  behavior	
  of	
  
many	
  thing””,	
  gain	
  important	
  insight	
  
§  Health	
  example	
  (understanding	
  the	
  cause	
  of	
  diseases/
comorbidi1es/indicators)	
  	
  
§  Real-­‐1me	
  ac1onable	
  insight	
  (Real-­‐1me	
  analy1c,	
  detect	
  and	
  react	
  in	
  
real-­‐1me)	
  
§  Health	
  example	
  (real-­‐1me	
  fall	
  detec1on	
  and	
  poten1al	
  reac1on	
  
for	
  aging	
  popula1on)	
  	
  	
  
§  Performance	
  op1miza1on	
  (configura1on,	
  energy,	
  health-­‐care)	
  
§  Health	
  example	
  (Improve	
  overall	
  healthcare	
  efficiency)	
  
§  Proac1ve	
  and	
  predic1ve	
  func1onal	
  applica1ons	
  
§  Health	
  example	
  (proac1ve	
  and	
  predic1on	
  iden1fica1on	
  of	
  
diagnos1c	
  in	
  healthcare	
  applica1ons	
  (before	
  thing	
  occur)	
  	
  
	
  
10	
  
Tradi9onal	
  methods	
   IoT/Big	
  data	
  
Centralize	
  	
   Distributed	
  	
  
More	
  power	
   More	
  machines	
  
Summarize	
  data	
   Keep	
  all	
  data	
  
Transform	
  and	
  store	
   Transform	
  on	
  demand	
  
Pre-­‐define	
  schema	
  	
   Flexible/no-­‐schema	
  
Move	
  data	
  toward	
  compute	
   Move	
  compute	
  towards	
  data	
  
Less	
  data/more	
  complex	
  algorithms	
  	
   More	
  data/simple	
  algorithms	
  	
  
Philosophical differences of Big data analytic
11	
  
§  More	
  distributed	
  processing	
  and	
  storage	
  of	
  the	
  massive	
  data	
  as	
  well	
  as	
  
cloud	
  func1onali1es	
  	
  
§  Processing	
  capabili1es	
  and	
  data	
  posi1oned	
  closer	
  to	
  users	
  
§  Distributed	
  and/or	
  edge	
  compu1ng	
  and	
  processing	
  of	
  IoT	
  big	
  data	
  
§  Distributed	
  storage	
  
§  Virtualiza9on	
  of	
  IoT	
  devices:	
  Access	
  to	
  advanced	
  resources/specialized	
  
hardware,	
  including	
  GPUs,	
  sensors,	
  etc.	
  
§  Interoperability:	
  Interoperability	
  between	
  cloud/IoT	
  services	
  and	
  
infrastructure	
  	
  
§  Accountability-­‐	
  Services	
  and	
  data	
  hosted	
  and	
  executed	
  across	
  borders	
  
§  Elas1city	
  and	
  scalability	
  of	
  cloud	
  data	
  management	
  	
  
§  Security	
  and	
  privacy	
  
§  Data	
  integrity,	
  localisa1on,	
  and	
  confiden1ality	
  
§  Data	
  localiza1on	
  is	
  one	
  of	
  the	
  biggest	
  challenges	
  	
  	
  
§  Security	
  and	
  privacy-­‐by-­‐design	
  (across	
  value	
  chains	
  including	
  SLAs,	
  
sogware	
  algorithms	
  and	
  new	
  data	
  management	
  models	
  
IoT big data and cloud challenges and requirements
12	
  
IoT Big data platform requirements
Intelligent and
dynamic
Scalable
Real-time Unified view
Distributed
Security and
privacy
13	
  
What cloud offers?
§  Dynamic	
  and	
  flexible	
  resources	
  sharing	
  plaiorm	
  
§  Offers	
  scalable,	
  elas1city	
  resources	
  and	
  data	
  management	
  	
  
§  	
  Loca1on	
  independent	
  can	
  be	
  access	
  from	
  any	
  where	
  
§  Reliable	
  and	
  easy	
  access	
  of	
  the	
  services	
  
§  Large	
  amount	
  of	
  compu1ng	
  and	
  storage	
  resources	
  
§  It	
  is	
  also	
  more	
  homogeneous	
  (unified)	
  
	
  
14	
  
Convergence of IoT-Big data and Cloud
	
  
"Cloud	
  compu*ng	
  a	
  new	
  business	
  model	
  and	
  management	
  (e.g.	
  data	
  
and	
  device)	
  paradigm	
  of	
  Internet	
  of	
  thing	
  and	
  Big	
  data"	
  
	
  
”IoT	
  Big	
  data	
  is	
  to	
  enlarge	
  the	
  opportuni*es	
  of	
  cloud	
  service	
  
provisioning	
  	
  
	
  
§  Convergence	
  Approaches	
  
§  Centralize	
  approach	
  (Bring	
  IoT	
  func1onali1es	
  in	
  Cloud)	
  
§  Distribute	
  approach	
  (Bring	
  Cloud	
  func1onali1es	
  in	
  IoT)	
  
	
  
15	
  
IoT-Big data-Cloud: Centralize approach
§  Bring	
  IoT	
  data	
  in	
  the	
  cloud	
  
§  Processing	
  and	
  compu1ng	
  the	
  data	
  and	
  deploy	
  management	
  
tools	
  in	
  cloud	
  
§  This	
  approach	
  this	
  good	
  if	
  service	
  are	
  provided	
  among	
  objects	
  
located	
  in	
  mul1ple	
  loca1on	
  	
  
	
  
IoT$Cloud$Pla+orm$
hosting databases applicationspartners SI
All devicesOur$managed$devices$
your$devices$
Cogni2ve$
capability$
16	
  
IoT-Big data- Cloud: Distributed approach
§  Edge/fog	
  compu1ng-­‐Stream	
  Processing	
  and	
  storage	
  of	
  data	
  
close	
  to	
  users/near	
  to	
  devices	
  
§  To	
  distribute	
  data	
  to	
  move	
  it	
  closer	
  to	
  the	
  end-­‐users	
  to	
  eliminate	
  
latency,	
  numerous	
  hop,	
  and	
  support	
  mobile	
  compu1ng	
  and	
  data	
  
streaming	
  	
  
§  Usability	
  	
  
§  High-­‐latency	
  and	
  real-­‐1me	
  ac1onable	
  insight	
  (the	
  data	
  flow	
  
to	
  fast	
  to	
  be	
  processed)	
  
§  Data/intelligence	
  context	
  are	
  geographically	
  distributed	
  	
  
§  The	
  datasets	
  have	
  strict	
  privacy,	
  security	
  and	
  regula1on	
  
constraints	
  that	
  prohibit	
  their	
  transfer	
  outside	
  of	
  the	
  paten	
  
domain	
  
§  Domain	
  specific	
  service	
  and	
  applica1ons	
  	
  	
  
	
  
iKaaS	
  (H2020	
  EU-­‐Japan)	
  	
  
IoT-­‐Big	
  and	
  Cloud	
  Project	
  
17
The	
  goal	
  of	
  iKaaS	
  project	
  is	
  to	
  combine	
  ubiquitous	
  
and	
  heterogeneous	
  sensing,	
  seman1c,	
  big	
  data	
  
and	
  cloud	
  compu7ng	
  technologies	
  in	
  a	
  plaiorm	
  
enabling	
  the	
  Internet	
  of	
  Things	
  	
  distributed	
  
process	
  consis1ng	
  of	
  con1nuous	
  	
  itera1ons	
  on	
  
data	
  inges1on,	
  data	
  storage,	
  analy7cs,	
  
knowledge	
  genera7on	
  and	
  knowledge	
  sharing	
  
phases,	
  as	
  founda1on	
  for	
  cross-­‐border	
  
informa1on	
  service	
  provision.	
  
	
   18	
Project	
  objec1ve	
  
Architecture	
  framework	
  (Distributed)	
19	
Local	
  CloudLocal	
  Cloud
KaaS
App.App.
Sensors
/IoT Devices
Sensors
/IoT Devices
Storage Storage
Data
Query
QueryQuery
Security
GW
Data
Data Data
Security
GW
Global	
  Cloud	
Knowledge	
   Knowledge	
  
Storage	
  
20	
  
iKaaS	
  
Programable
Service	
  logic	
  
	
  
Publish	
  sensor	
  needs,	
  
Privacy	
  needs,	
  RT	
  needs,	
  
Reliability	
  needs	
  (constraints)	
  
Alloca1on	
  op1mizer	
  
Alloca1on	
  	
  
decision	
  
Cloud,	
  data	
  
center	
  
Cloud	
  
Controler	
  
Move	
  to	
  the	
  local	
  
Cloud	
  A	
  
…	
  or	
  stay	
  in	
  local	
  Cloud	
  
Service	
  and	
  processing	
  migra1on	
  
Cloud,	
  data	
  
center	
  
Move	
  to	
  the	
  Global	
  	
  
Cloud	
  B	
  
…	
  or	
  stay	
  in	
  local	
  Cloud	
  
•  Smart	
  service	
  logic	
  	
  
–  Autonomously	
  analyse	
  applica1on	
  requirements,	
  user	
  preferences	
  
–  Register	
  the	
  services/deployment	
  of	
  services	
  	
  
•  Alloca9on	
  manager	
  
–  	
  The	
  most	
  appropriate	
  deployment	
  of	
  service	
  must	
  achieve	
  the	
  best	
  
balance	
  among	
  cloud	
  resources,	
  system	
  performance,	
  quality	
  of	
  
service	
  and	
  cost.	
  	
  
–  Appropriate	
  service	
  execu1on	
  	
  	
  
•  Service/task	
  Manager	
  (Query,	
  control,	
  and	
  reconfigura1on)	
  
–  Analysis	
  of	
  the	
  applica1on	
  request(s)	
  using	
  iKaaS	
  service	
  model/
templates;	
  flexible/autonomic	
  selec1on	
  of	
  more	
  appropriate	
  cloud	
  
resources	
  
–  Reconfigure	
  the	
  service	
  logic	
  on	
  run-­‐1me	
  (e.g,	
  dynamically	
  changes	
  
the	
  services/business	
  logic)	
  
–  Synchroniza1on	
  of	
  the	
  service	
  logic	
  deployment,	
  service	
  migra1on,	
  
decision	
  between	
  local	
  and	
  global	
  cloud	
  	
  
	
  
Service	
  deployment	
  and	
  orchestra1on	
  	
  
Distributed	
  execu1on	
  environment	
  
22	
Service	
  
Query	
  
Service	
  query	
  
(Query	
  
control)	
  
service	
  logic	
  
Smart	
  logic	
  
Local	
  Cloud	
  Global	
  Cloud	
  
Configura1on	
  
manager	
  	
  
Configura1on	
  
and	
  alloca1on	
  	
  
Manager	
  	
  
Dependent	
  Independent	
  
Migra9on	
  	
  
Synchroniza9on	
  	
  	
  
Programmable	
  
applica1on	
  logic	
  
Service	
  
catalogue	
  
Service	
  	
  
Catalogue	
  
Service/task	
  Manager	
  ervice/task	
  Manager	
  
23	
  
Multi-scale service migration
§  Migra1on	
  of	
  rela1onship	
  logic	
  to	
  local	
  cloud	
  	
  
Service	
  request
Service	
  
component	
  
migration
Service	
  results
Service	
  orchestration
Service	
  orchestration
Local	
  Cloud
Service	
  
execution
Local	
  Cloud
Service	
  
execution
Local	
  Cloud
Service	
  
execution
uCore Framework
Analysis
Decision	
  Making
Service	
  Logic	
  
description
Monitoring
Learning
Service	
  component	
  
results
Cognitive	
  Engine
Service	
  and	
  
associated	
  meta-­‐data
Global	
  Cloud
Smart	
  
Virtual	
  
ObjectsComputing	
  in	
  the	
  
Global	
  Cloud
Mul1-­‐scale	
  applica1on	
  migra1on	
  
My	
  laptop	
  
Gateway1	
  
Server	
  
applica1on	
  
iKaaS	
  Component	
  
Temp.	
  
sensor	
  
Gateway2	
  
Temp.	
  
sensor	
  
Gateway3	
  
Temp.	
  
sensor	
  
1ms	
  readings	
  
1ms	
  readings	
  
1ms	
  readings	
  
iKaaS	
  
Component	
  
	
  Local	
  
Proc.	
  
Local	
  
Proc.	
  
Local	
  
Proc.	
  
Daily	
  computa1on	
  	
  
results	
  
Final	
  
result	
  
Service	
  	
  
migra1on	
  
Service	
  
migra1on	
  
In	
  red:	
  applica1on	
  logic	
  deployment	
  
In	
  blue:	
  data	
  gathering	
  and	
  consolida1ng	
  
• Applica1on’s	
  logic	
  can	
  be	
  migrated	
  near	
  the	
  data	
  sources	
  
• mul1-­‐scale	
  (recursive)	
  process:	
  the	
  applica1on’s	
  logic	
  can	
  be	
  broken	
  down	
  again	
  	
  
and	
  further	
  migrated	
  
Security	
  Gateway	
  
Global	
  Cloud	
Local	
  Cloud	
Privacy	
  
Policy	
Security	
  
Policy
Global	
  Cloud	
  
	
  
	
Security	
  Gateway	
  (2)	
•  Security	
  and	
  Privacy	
  by	
  Design	
  Concept	
  
•  Main	
  Func1ons:	
  
–  Policy	
  Management	
  &	
  Nego1a1on	
  (Cross-­‐Border)	
  
–  Authen1ca1on	
  and	
  Access	
  Control	
  (Service	
  Level)	
  
–  Transforma1on	
  of	
  Data	
  (Privacy	
  Preserving	
  Way)	
  
•  Applica1on	
  to	
  Cross-­‐border	
  Scenario	
  	
  
26	
Security	
  GW	
Local	
  
App.	
Security	
  GW	
  
	
  
	
Internal	
  Use	
External	
  
App.	
Cross-­‐Border	
  Use	
Local	
  Cloud	
Data	
  Transfer	
Policy	
  Nego1a1on	
Local	
  Cloud
Design	
  of	
  the	
  Security	
  Gateway	
  	
  	
  	
  
Security Gateway (3)
Privacy Certificate DB	
Privacy CA	
Local Cloud	
Local Cloud DBs	
 Local Query Controller
Privacy Policy DB	
Policy DBs	
Security Gateway	
Token DB Security Policy DB	
Global Cloud	
Privacy Control Functions	
Cache Policy DB	
Owner DB
Key DB
Access Control Functions	
Cache DB	
Cache Manager	
Query Control Functions	
Data Processing Functions	
Global Platform	
Application
28	
  
§  Procedure	
  
§  Token	
  Issuance	
  
§  I.	
  An	
  applica1on	
  requests	
  the	
  privacy	
  CA	
  to	
  issue	
  the	
  privacy	
  cer1ficate.	
  
§  II.	
  The	
  applica1on	
  searches	
  the	
  security	
  gateway	
  of	
  the	
  domain	
  where	
  there	
  are	
  the	
  local	
  cloud	
  DBs	
  suited	
  for	
  the	
  objec1ve	
  with	
  using	
  the	
  
query	
  control	
  func1ons	
  on	
  the	
  global	
  plaiorm.	
  
§  III.	
  The	
  applica1on	
  calls	
  func1on	
  Issuance	
  of	
  Token	
  that	
  the	
  security	
  gateway	
  provides.	
  The	
  applica1on	
  then	
  specifies	
  the	
  DB	
  IDs	
  of	
  the	
  local	
  
cloud	
  DBs	
  that	
  it	
  wants	
  access	
  to,	
  and	
  sends	
  the	
  privacy	
  cer1ficate.	
  
§  IV.	
  The	
  security	
  gateway	
  confirms	
  the	
  values	
  of	
  parameters	
  CA	
  Domain	
  Name	
  and	
  Expires	
  listed	
  on	
  the	
  privacy	
  cer1ficate	
  to	
  verify	
  the	
  
correctness	
  of	
  the	
  cer1ficate.	
  
§  V.	
  The	
  security	
  gateway	
  checks	
  the	
  values	
  of	
  Applica1on	
  IP,	
  LC	
  Domain	
  Names	
  and	
  LC	
  DB	
  IDs	
  listed	
  on	
  the	
  privacy	
  cer1ficate	
  to	
  validate	
  the	
  
applica1on	
  and	
  the	
  request.	
  
§  VI.	
  The	
  security	
  gateway	
  creates	
  a	
  token	
  and	
  returns	
  it	
  to	
  the	
  applica1on.	
  
§  Data	
  Request	
  
§  I.	
  An	
  applica1on	
  generates	
  the	
  MAC	
  of	
  the	
  SGW-­‐query	
  with	
  using	
  the	
  token,	
  which	
  is	
  a	
  common	
  key.	
  
§  II.	
  The	
  applica1on	
  calls	
  func1on	
  Getng	
  Data	
  that	
  the	
  security	
  gateway	
  provides	
  and	
  transmits	
  SGW-­‐query	
  and	
  the	
  MAC	
  to	
  the	
  security	
  
gateway.	
  
§  III.	
  The	
  security	
  gateway	
  extracts	
  the	
  corresponding	
  token	
  from	
  the	
  token	
  DB	
  with	
  the	
  values	
  of	
  the	
  Applica1on	
  ID	
  and	
  Applica1on	
  IP	
  headers	
  
and	
  checks	
  the	
  expired	
  date	
  of	
  the	
  token.	
  
§  IV.	
  The	
  security	
  gateway	
  generates	
  the	
  MAC	
  from	
  the	
  token	
  and	
  the	
  SGW-­‐query	
  to	
  verify	
  the	
  authen1city	
  of	
  the	
  query.	
  The	
  value	
  of	
  the	
  Time	
  
Stamp	
  header	
  is	
  also	
  confirmed.	
  
§  V.	
  The	
  security	
  gateway	
  transmits	
  the	
  LCD-­‐query	
  to	
  the	
  local	
  query	
  controller.	
  
§  VI.	
  When	
  the	
  data	
  are	
  returned	
  from	
  the	
  local	
  cloud	
  DBs,	
  the	
  security	
  gateway	
  confirms	
  the	
  privacy	
  type	
  of	
  the	
  DBs	
  while	
  searching	
  the	
  token	
  
DB.	
  
§  VII.	
  If	
  the	
  data	
  stored	
  in	
  the	
  non-­‐privacy	
  DB	
  are	
  returned,	
  the	
  security	
  gateway	
  returns	
  the	
  data	
  to	
  the	
  applica1on	
  without	
  doing	
  anything.	
  
Otherwise,	
  Steps	
  8-­‐-­‐11	
  are	
  carried	
  out.	
  
§  VIII.	
  The	
  security	
  gateway	
  extracts	
  the	
  corresponding	
  owner	
  IDs	
  from	
  the	
  owner	
  DB	
  with	
  using	
  the	
  value	
  of	
  the	
  Owner	
  AGributes	
  header.	
  
§  IX.	
  The	
  security	
  gateway	
  searches	
  the	
  privacy	
  policy	
  with	
  using	
  the	
  extracted	
  owner	
  IDs	
  and	
  the	
  values	
  of	
  the	
  Applica1on	
  ID	
  and	
  LC	
  DB	
  IDs	
  
headers	
  and	
  confirms	
  the	
  status	
  of	
  the	
  consent	
  of	
  the	
  corresponding	
  data	
  owners.	
  
§  X.	
  The	
  security	
  gateway	
  extracts	
  the	
  data	
  such	
  that	
  the	
  data	
  owner	
  agrees	
  on	
  the	
  transfer	
  and	
  returns	
  the	
  extracted	
  data	
  to	
  the	
  applica1on.	
Security Gateway (4)
•  Example	
  of	
  Security	
  Policy	
  
–  Token	
  Configura1on	
  (such	
  as	
  period	
  and	
  accessible	
  informa1on)	
  
should	
  be	
  defined	
  for	
  each	
  applica1on	
  category	
  and	
  country	
  of	
  the	
  
domain	
  that	
  applica1on	
  is	
  executed.	
  	
  
	
  
Level	
 DB	
  1	
  	
 DB	
  2	
 ・・・	
   DB	
  N	
Applica1on	
  A	
  
Administrat
or	
  1	
  
1	
  
UK	
  0	
  /	
  JP	
  
2mo	
  
Non-­‐Privacy	
  
UK	
  3h	
  /	
  JP	
  
3h	
  
Non-­‐privacy	
・・・	
  
UK	
  0	
  /	
  JP	
  0	
  
Privacy	
  
Administrat
or	
  2	
2	
UK	
  1h	
  /	
  JP	
  
2h	
  
Privacy	
UK	
  5h	
  /	
  JP	
  0	
  
Non-­‐privacy	
・・・	
UK	
  0	
  	
  /	
  JP	
  0	
  
Privacy	
	
	
  
	
  
	
	
Administrat
or	
  M	
M	
UK	
  0	
  /	
  JP	
  0	
  
Non-­‐privacy	
UK	
  1h	
  /	
  JP	
  0	
  
Non-­‐privacy	
・・・	
UK	
  0	
  /	
  JP	
  0	
  
Privacy	
Security	
  Gateway	
  (5)	
  
30	
  
	
  
§  Performance	
  Evalua1on	
  Results	
  
§  Transac1on	
  1me	
  of	
  data	
  collec1on	
  is	
  prac1cal.	
  
§  Cache	
  func1on	
  is	
  effec1ve	
  for	
  reducing	
  the	
  transac1on	
  1me.	
  
Security Gateway(6)
#	
  of	
  Data	
   Non-­‐Private	
 Private	
 Using	
  Cache	
  Func.	
1000	
 16.868171	
   215.650792	
   3.426036	
  
10000	
 57.940439	
   254.608338	
   5.528918	
  
100000	
 504.188900	
   776.667116	
   21.692454	
  
1000000	
 5109.974000	
   5872.079780	
   155.043988	
  
31	
  
Take away message
§  Convergence is everywhere
§  If you start innovation think on the how your
business will convergence and scale
§  When we talk IoT, it is actually the large-
scale
§  NEED of large-scale IoT is to exploit Big data
for smart IoT services that processed and
executed on the cloud to derive business
value insight

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Internet of Things (IoT) is a King, Big data is a Queen and Cloud is a Palace

  • 1. 1  Intelligent Knowledge-as-a-Service IoT is a King, Big data is a Queen and Cloud is a Palace Abdur Rahim Innotec21 GmbH, Germany Create-Net, Italy Abdur.rahim@create-net.org Acknowledgements- iKaaS Partners (KDDI and other partnes)
  • 2. 2   §  Motivation §  Convergence/opportunities/applications §  Challenges and requirements §  Convergence approach §  iKaaS EU-Japan project §  Conclusion Outline
  • 3. 3   Convergence of Technologies Source-IDC    
  • 4. 4   Where is the value of IoT? §  In  the  past,  connec1vity  and  number  of  the  devices  were  the   main  driver  of  IoT   §  Data  is  nothing  without  big  business  value  insight   §   IoT  without  BIG  DATA  is  first  genera1on  of  IoT  
  • 5. 5   The real value is not just sheer number of connected devices and data §  The  real  opportunity  is  improved  business  value-­‐new   revenue  models,  lower  cost,  improved  client  experience,   beGer  insight  improve  outcomes     Source-IDC
  • 6. 6   Big data-how we understand it Source:  Nenad  
  • 7. 7   IoT in BIG data §  IoT  presents  challenges  in  combina1on  of  all  BIG  data   characteris1cs  (3Vs/4Vs)     §  Most  challenging  IoT  applica1ons  match  with  either  or  both   Velocity  &  Volume  and  some1mes  also  Variety  (situa1on  and   context)   §  Velocity  driven-­‐applica1on   §  A  wearable  sensor  produces  about  55  million  data  points  pro  day   (challenge  for  storage),  whereas  some  medical  wearable's  (like   ECG)  produce  up  to  1000  events  per  second  (challenge  for  real-­‐ 1me  processing)   §  Volume  driven-­‐applica1ons   §  GE  each  day  gathers  50  million  pieces  of  data  from  10  million   sensors,  off  equipment  worth  $1  trillion    
  • 8. 8   Typical IoT applications Source:  Harvad  business  review    
  • 9. 9   IoT BIG data applications   §  Massive  monitoring/Deep  understanding  (observe  of  behavior  of   many  thing””,  gain  important  insight   §  Health  example  (understanding  the  cause  of  diseases/ comorbidi1es/indicators)     §  Real-­‐1me  ac1onable  insight  (Real-­‐1me  analy1c,  detect  and  react  in   real-­‐1me)   §  Health  example  (real-­‐1me  fall  detec1on  and  poten1al  reac1on   for  aging  popula1on)       §  Performance  op1miza1on  (configura1on,  energy,  health-­‐care)   §  Health  example  (Improve  overall  healthcare  efficiency)   §  Proac1ve  and  predic1ve  func1onal  applica1ons   §  Health  example  (proac1ve  and  predic1on  iden1fica1on  of   diagnos1c  in  healthcare  applica1ons  (before  thing  occur)      
  • 10. 10   Tradi9onal  methods   IoT/Big  data   Centralize     Distributed     More  power   More  machines   Summarize  data   Keep  all  data   Transform  and  store   Transform  on  demand   Pre-­‐define  schema     Flexible/no-­‐schema   Move  data  toward  compute   Move  compute  towards  data   Less  data/more  complex  algorithms     More  data/simple  algorithms     Philosophical differences of Big data analytic
  • 11. 11   §  More  distributed  processing  and  storage  of  the  massive  data  as  well  as   cloud  func1onali1es     §  Processing  capabili1es  and  data  posi1oned  closer  to  users   §  Distributed  and/or  edge  compu1ng  and  processing  of  IoT  big  data   §  Distributed  storage   §  Virtualiza9on  of  IoT  devices:  Access  to  advanced  resources/specialized   hardware,  including  GPUs,  sensors,  etc.   §  Interoperability:  Interoperability  between  cloud/IoT  services  and   infrastructure     §  Accountability-­‐  Services  and  data  hosted  and  executed  across  borders   §  Elas1city  and  scalability  of  cloud  data  management     §  Security  and  privacy   §  Data  integrity,  localisa1on,  and  confiden1ality   §  Data  localiza1on  is  one  of  the  biggest  challenges       §  Security  and  privacy-­‐by-­‐design  (across  value  chains  including  SLAs,   sogware  algorithms  and  new  data  management  models   IoT big data and cloud challenges and requirements
  • 12. 12   IoT Big data platform requirements Intelligent and dynamic Scalable Real-time Unified view Distributed Security and privacy
  • 13. 13   What cloud offers? §  Dynamic  and  flexible  resources  sharing  plaiorm   §  Offers  scalable,  elas1city  resources  and  data  management     §   Loca1on  independent  can  be  access  from  any  where   §  Reliable  and  easy  access  of  the  services   §  Large  amount  of  compu1ng  and  storage  resources   §  It  is  also  more  homogeneous  (unified)    
  • 14. 14   Convergence of IoT-Big data and Cloud   "Cloud  compu*ng  a  new  business  model  and  management  (e.g.  data   and  device)  paradigm  of  Internet  of  thing  and  Big  data"     ”IoT  Big  data  is  to  enlarge  the  opportuni*es  of  cloud  service   provisioning       §  Convergence  Approaches   §  Centralize  approach  (Bring  IoT  func1onali1es  in  Cloud)   §  Distribute  approach  (Bring  Cloud  func1onali1es  in  IoT)    
  • 15. 15   IoT-Big data-Cloud: Centralize approach §  Bring  IoT  data  in  the  cloud   §  Processing  and  compu1ng  the  data  and  deploy  management   tools  in  cloud   §  This  approach  this  good  if  service  are  provided  among  objects   located  in  mul1ple  loca1on       IoT$Cloud$Pla+orm$ hosting databases applicationspartners SI All devicesOur$managed$devices$ your$devices$ Cogni2ve$ capability$
  • 16. 16   IoT-Big data- Cloud: Distributed approach §  Edge/fog  compu1ng-­‐Stream  Processing  and  storage  of  data   close  to  users/near  to  devices   §  To  distribute  data  to  move  it  closer  to  the  end-­‐users  to  eliminate   latency,  numerous  hop,  and  support  mobile  compu1ng  and  data   streaming     §  Usability     §  High-­‐latency  and  real-­‐1me  ac1onable  insight  (the  data  flow   to  fast  to  be  processed)   §  Data/intelligence  context  are  geographically  distributed     §  The  datasets  have  strict  privacy,  security  and  regula1on   constraints  that  prohibit  their  transfer  outside  of  the  paten   domain   §  Domain  specific  service  and  applica1ons        
  • 17. iKaaS  (H2020  EU-­‐Japan)     IoT-­‐Big  and  Cloud  Project   17
  • 18. The  goal  of  iKaaS  project  is  to  combine  ubiquitous   and  heterogeneous  sensing,  seman1c,  big  data   and  cloud  compu7ng  technologies  in  a  plaiorm   enabling  the  Internet  of  Things    distributed   process  consis1ng  of  con1nuous    itera1ons  on   data  inges1on,  data  storage,  analy7cs,   knowledge  genera7on  and  knowledge  sharing   phases,  as  founda1on  for  cross-­‐border   informa1on  service  provision.     18 Project  objec1ve  
  • 19. Architecture  framework  (Distributed) 19 Local  CloudLocal  Cloud KaaS App.App. Sensors /IoT Devices Sensors /IoT Devices Storage Storage Data Query QueryQuery Security GW Data Data Data Security GW Global  Cloud Knowledge   Knowledge   Storage  
  • 20. 20   iKaaS   Programable Service  logic     Publish  sensor  needs,   Privacy  needs,  RT  needs,   Reliability  needs  (constraints)   Alloca1on  op1mizer   Alloca1on     decision   Cloud,  data   center   Cloud   Controler   Move  to  the  local   Cloud  A   …  or  stay  in  local  Cloud   Service  and  processing  migra1on   Cloud,  data   center   Move  to  the  Global     Cloud  B   …  or  stay  in  local  Cloud  
  • 21. •  Smart  service  logic     –  Autonomously  analyse  applica1on  requirements,  user  preferences   –  Register  the  services/deployment  of  services     •  Alloca9on  manager   –   The  most  appropriate  deployment  of  service  must  achieve  the  best   balance  among  cloud  resources,  system  performance,  quality  of   service  and  cost.     –  Appropriate  service  execu1on       •  Service/task  Manager  (Query,  control,  and  reconfigura1on)   –  Analysis  of  the  applica1on  request(s)  using  iKaaS  service  model/ templates;  flexible/autonomic  selec1on  of  more  appropriate  cloud   resources   –  Reconfigure  the  service  logic  on  run-­‐1me  (e.g,  dynamically  changes   the  services/business  logic)   –  Synchroniza1on  of  the  service  logic  deployment,  service  migra1on,   decision  between  local  and  global  cloud       Service  deployment  and  orchestra1on    
  • 22. Distributed  execu1on  environment   22 Service   Query   Service  query   (Query   control)   service  logic   Smart  logic   Local  Cloud  Global  Cloud   Configura1on   manager     Configura1on   and  alloca1on     Manager     Dependent  Independent   Migra9on     Synchroniza9on       Programmable   applica1on  logic   Service   catalogue   Service     Catalogue   Service/task  Manager  ervice/task  Manager  
  • 23. 23   Multi-scale service migration §  Migra1on  of  rela1onship  logic  to  local  cloud     Service  request Service   component   migration Service  results Service  orchestration Service  orchestration Local  Cloud Service   execution Local  Cloud Service   execution Local  Cloud Service   execution uCore Framework Analysis Decision  Making Service  Logic   description Monitoring Learning Service  component   results Cognitive  Engine Service  and   associated  meta-­‐data Global  Cloud Smart   Virtual   ObjectsComputing  in  the   Global  Cloud
  • 24. Mul1-­‐scale  applica1on  migra1on   My  laptop   Gateway1   Server   applica1on   iKaaS  Component   Temp.   sensor   Gateway2   Temp.   sensor   Gateway3   Temp.   sensor   1ms  readings   1ms  readings   1ms  readings   iKaaS   Component    Local   Proc.   Local   Proc.   Local   Proc.   Daily  computa1on     results   Final   result   Service     migra1on   Service   migra1on   In  red:  applica1on  logic  deployment   In  blue:  data  gathering  and  consolida1ng   • Applica1on’s  logic  can  be  migrated  near  the  data  sources   • mul1-­‐scale  (recursive)  process:  the  applica1on’s  logic  can  be  broken  down  again     and  further  migrated  
  • 25. Security  Gateway   Global  Cloud Local  Cloud Privacy   Policy Security   Policy
  • 26. Global  Cloud     Security  Gateway  (2) •  Security  and  Privacy  by  Design  Concept   •  Main  Func1ons:   –  Policy  Management  &  Nego1a1on  (Cross-­‐Border)   –  Authen1ca1on  and  Access  Control  (Service  Level)   –  Transforma1on  of  Data  (Privacy  Preserving  Way)   •  Applica1on  to  Cross-­‐border  Scenario     26 Security  GW Local   App. Security  GW     Internal  Use External   App. Cross-­‐Border  Use Local  Cloud Data  Transfer Policy  Nego1a1on Local  Cloud
  • 27. Design  of  the  Security  Gateway         Security Gateway (3) Privacy Certificate DB Privacy CA Local Cloud Local Cloud DBs Local Query Controller Privacy Policy DB Policy DBs Security Gateway Token DB Security Policy DB Global Cloud Privacy Control Functions Cache Policy DB Owner DB Key DB Access Control Functions Cache DB Cache Manager Query Control Functions Data Processing Functions Global Platform Application
  • 28. 28   §  Procedure   §  Token  Issuance   §  I.  An  applica1on  requests  the  privacy  CA  to  issue  the  privacy  cer1ficate.   §  II.  The  applica1on  searches  the  security  gateway  of  the  domain  where  there  are  the  local  cloud  DBs  suited  for  the  objec1ve  with  using  the   query  control  func1ons  on  the  global  plaiorm.   §  III.  The  applica1on  calls  func1on  Issuance  of  Token  that  the  security  gateway  provides.  The  applica1on  then  specifies  the  DB  IDs  of  the  local   cloud  DBs  that  it  wants  access  to,  and  sends  the  privacy  cer1ficate.   §  IV.  The  security  gateway  confirms  the  values  of  parameters  CA  Domain  Name  and  Expires  listed  on  the  privacy  cer1ficate  to  verify  the   correctness  of  the  cer1ficate.   §  V.  The  security  gateway  checks  the  values  of  Applica1on  IP,  LC  Domain  Names  and  LC  DB  IDs  listed  on  the  privacy  cer1ficate  to  validate  the   applica1on  and  the  request.   §  VI.  The  security  gateway  creates  a  token  and  returns  it  to  the  applica1on.   §  Data  Request   §  I.  An  applica1on  generates  the  MAC  of  the  SGW-­‐query  with  using  the  token,  which  is  a  common  key.   §  II.  The  applica1on  calls  func1on  Getng  Data  that  the  security  gateway  provides  and  transmits  SGW-­‐query  and  the  MAC  to  the  security   gateway.   §  III.  The  security  gateway  extracts  the  corresponding  token  from  the  token  DB  with  the  values  of  the  Applica1on  ID  and  Applica1on  IP  headers   and  checks  the  expired  date  of  the  token.   §  IV.  The  security  gateway  generates  the  MAC  from  the  token  and  the  SGW-­‐query  to  verify  the  authen1city  of  the  query.  The  value  of  the  Time   Stamp  header  is  also  confirmed.   §  V.  The  security  gateway  transmits  the  LCD-­‐query  to  the  local  query  controller.   §  VI.  When  the  data  are  returned  from  the  local  cloud  DBs,  the  security  gateway  confirms  the  privacy  type  of  the  DBs  while  searching  the  token   DB.   §  VII.  If  the  data  stored  in  the  non-­‐privacy  DB  are  returned,  the  security  gateway  returns  the  data  to  the  applica1on  without  doing  anything.   Otherwise,  Steps  8-­‐-­‐11  are  carried  out.   §  VIII.  The  security  gateway  extracts  the  corresponding  owner  IDs  from  the  owner  DB  with  using  the  value  of  the  Owner  AGributes  header.   §  IX.  The  security  gateway  searches  the  privacy  policy  with  using  the  extracted  owner  IDs  and  the  values  of  the  Applica1on  ID  and  LC  DB  IDs   headers  and  confirms  the  status  of  the  consent  of  the  corresponding  data  owners.   §  X.  The  security  gateway  extracts  the  data  such  that  the  data  owner  agrees  on  the  transfer  and  returns  the  extracted  data  to  the  applica1on. Security Gateway (4)
  • 29. •  Example  of  Security  Policy   –  Token  Configura1on  (such  as  period  and  accessible  informa1on)   should  be  defined  for  each  applica1on  category  and  country  of  the   domain  that  applica1on  is  executed.       Level DB  1   DB  2 ・・・   DB  N Applica1on  A   Administrat or  1   1   UK  0  /  JP   2mo   Non-­‐Privacy   UK  3h  /  JP   3h   Non-­‐privacy ・・・   UK  0  /  JP  0   Privacy   Administrat or  2 2 UK  1h  /  JP   2h   Privacy UK  5h  /  JP  0   Non-­‐privacy ・・・ UK  0    /  JP  0   Privacy     Administrat or  M M UK  0  /  JP  0   Non-­‐privacy UK  1h  /  JP  0   Non-­‐privacy ・・・ UK  0  /  JP  0   Privacy Security  Gateway  (5)  
  • 30. 30     §  Performance  Evalua1on  Results   §  Transac1on  1me  of  data  collec1on  is  prac1cal.   §  Cache  func1on  is  effec1ve  for  reducing  the  transac1on  1me.   Security Gateway(6) #  of  Data   Non-­‐Private Private Using  Cache  Func. 1000 16.868171   215.650792   3.426036   10000 57.940439   254.608338   5.528918   100000 504.188900   776.667116   21.692454   1000000 5109.974000   5872.079780   155.043988  
  • 31. 31   Take away message §  Convergence is everywhere §  If you start innovation think on the how your business will convergence and scale §  When we talk IoT, it is actually the large- scale §  NEED of large-scale IoT is to exploit Big data for smart IoT services that processed and executed on the cloud to derive business value insight