SlideShare a Scribd company logo
0Copyright © 2013 Tata Consultancy Services Limited u-World 2013, 22nd June 2013
Distributed Edge-Computing for Internet-
of-Things
Arpan Pal
Principal Scientist and Research Head
Innovation Lab, Kolkata
Tata Consultancy Services
With Arijit Mukherjee and Soma Bandyopadhyay
Innovation Lab, Kolkata
Outline
Analytics in Internet of Things
Requirements and Challenges
Challenges and Solution Approach
Innovation@TCS
Analytics in Internet-of-Things
3
Signal
Processing
Internet-of-Things - towards Intelligent Infrastructure
Sense
Extract
Analyze
Respond
Learn
Monitor
Intelligent
Infra
@Home
@Building
@Vehicle
@Utility
@Mobile
@Store
@Road
“Intelligent” (Cyber) “Infrastructure” (Physical)
APPLICATION SERVICES
BACK-END PLATFORM
INTERNET
GATEWAY
Sense
Extract
Analyze
Respond
Communication
Computing
4
IoT Platform from TCS
Internet
End Users
Administrators
Device Integration & Management Services
Analytics Services
Application Services
Storage
Messaging & Event Distribution Services
ApplicationServices
Presentation Services
Application Support Services
Middleware
Edge Gateway
Sensors
Internet
Back-end on Cloud
RIPSAC – Real-time Integrated Platform for Services & AnalytiCs
Traditional
Internet
 Service Delivery
Platform & App
Development
Platform
 Security/Privacy
Framework
 Lightweight M2M
Protocols
 Analytics-as-a-
Service
 Social Network
Integration
 SDKs and APIs for
App developer
Grid
Computing
Components
5
Utility
Appliances
Smart
Plugs
Intelligent
Gateway
Smart
Meter
Demand Forecasting
Demand Response
Appliance Management
Consumption View
Appliance Scheduling
On-off Control
Social Network
Integration
Consumer Home
Analytics
Home Energy Management
RIPSAC
6
Healthcare – Remote Medical Consultation
ECG
Body Fat Analyzer
Blood Pressure
Monitor
Pulse OxyMeter
Mobile gateway
Patient
Records
Health Center / Home
Expert Doctor
Analytics
and
Decision
Support
Systems
Wireless gateway
7
Communication
& Reporting
Forecast 1
Forecast 2
Adaptive
Combination
Forecast 3
.
.
Cloud Services for Adaptive Wind Forecasting
Protocol
Convertor
SCADA
Workstation 2
SCADA
Workstation 1
Wind Operator Control Room
Internet
TCS ReSolver ABG Model on RIPSAC
•Adaptive forecast
•Program maintenance
•Reporting
Requirements and Challenges
9
Grid Computing and IoT
 It is all about Intelligent Systems
 Intelligence comes from Analytics
 Need for crunching huge amount of sensor data and
respond in real-time
 Needs huge computing infrastructure in cloud
 Another option is to distribute computing load to the edge
devices
10
The Grid in IoT is in the Edge - Fog Computing
• Flavio Bonomi et.al. MCC2012, Helsinki, Finland
11
Advantages
 Edge Devices computing power remain unused most of the
time
o Free Computing resource for the grid
o Potentially millions of ~1GHz Processors on the grid depending upon
use case
 Energy cost at edge is typically at consumer rates <<
Energy cost at cloud which is at Enterprise rates
o Energy cost account for 50% of Data Center Opex
12
Challenges
• Communication and Energy Cost incurred at Edge
• How to reduce the cost of Communication
• How to preserve the Battery power
• Should not effect the user experience during normal usage
• How to sense idle time in real-time and allocate job / distribute
data optimally
• Smartphones as edge devices
• Incentivisation for users to allow this
• Edge devices are typically constrained in memory and have
variety of hardware and software flavors
• Need to factor in device capability in job scheduling design
• Need to create common middleware framework for job
distribution / execution
Solution Approach
14
Solution Approach
• Agent-based grid Computing using CONDOR
• Need for agents in diverse types of edge devices via a common framework
• Min-Jen Tsai, ,Yuan-Fu Luo , Expert Systems with Applications, Volume 36, Issue 7,
Sept. 2009, Elsevier
15
Framework for Distributed Computing in IoT
16
Communication Aspect- Replace HTTP
• http://people.inf.ethz.ch/mkovatsc/californium.php
• Ralf Koetter, Muriel Medard, 2003 IEEE/ACM transaction http://web.mit.edu/medard/www/NWCFINAL.pdf
• Bandyopadhyay, S. and Bhattacharyya, A. Lightweight Internet protocols for web enablement of sensors using constrained gateway devices. In Proc.
International Conference on Computing, Networking and Communications (ICNC), 2013, San Diego, CA, IEEE(2013), 334 – 340
Use suitable lightweight application protocol between edge devices and core network
17
Computation Aspect
The Wind Turbine Problem
 N predictors
 Computation (in R) takes 10 min for each
predictor
 Prediction cycle starts every 30 mins
 Current solution uses HA Proxy to schedule jobs
to Rserve instances.
18
Inferences
 CPU utilization better in Condor
 Turn-around time are almost equivalent
 Condor starts performing better with more
nodes
 Further advantages in Condor w.r.t
– Heterogeneity
– Versatility
– Matchmaking & scheduling
Computation Aspect – Need for a Scheduler
Scheduler is Important
Innovation @TCS
20
Tata Consultancy Services Ltd. (TCS)
 Pioneer & Leader in Indian IT
TCS was established in 1968
 One of the top ranked global software service provider
 Largest Software service provider in Asia
 250,000+ associates
 USD 10B + annual revenue
 Global presence
 First Software R&D Center in India
- 20 -
21
Innovation@TCS - Innovation Labs
Bangalore, India1
TCS Innovation Labs - Bangalore
Chennai, India2
TCS Innovation Labs - Chennai
TCS Innovation Labs - Retail
TCS Innovation Labs - Travel & Hospitality
TCS Innovation Labs - Insurance
TCS Innovation Labs - Web 2.0
TCS Innovation Labs - Telecom
Cincinnati, USA3
TCS Innovation Labs - Cincinnati
Delhi, India4
TCS Innovation Labs - Delhi
Hyderabad, India5
TCS Innovation Labs - Hyderabad
TCS Innovation Labs - CMC
Kolkata, India6
TCS Innovation Labs - Kolkata
Mumbai, India7
TCS Innovation Labs - Mumbai
TCS Innovation Labs - Performance Engineering
Peterborough, UK8
TCS Innovation Labs - Peterborough
Pune, India9
TCS Innovation Labs - TRDDC - Process Engineering
TCS Innovation Labs - TRDDC - Software Engineering
TCS Innovation Labs - TRDDC - Systems Research
TCS Innovation Labs - Engineering & Industrial Services
1 2
3
4
5
97
6
8
2000+ Associates in Research, Development and Asset Creation
19 Innovation Labs
Thank You
arpan.pal@tcs.com

More Related Content

PPTX
Arpan pal gridcomputing_iot_uworld2013
PPTX
Arpan pal gridcomputing_iot_uworld2013
PPTX
Arpan pal gridcomputing_iot_uworld2013
PPTX
Arpan pal uworld2013
PDF
Vertex Perspectives | AI Optimized Chipsets | Part III
PDF
IoT and the Role of Platforms
PPTX
Algorithmia at HackerNews Meetup Seattle
PDF
Vertex perspectives artificial intelligence
Arpan pal gridcomputing_iot_uworld2013
Arpan pal gridcomputing_iot_uworld2013
Arpan pal gridcomputing_iot_uworld2013
Arpan pal uworld2013
Vertex Perspectives | AI Optimized Chipsets | Part III
IoT and the Role of Platforms
Algorithmia at HackerNews Meetup Seattle
Vertex perspectives artificial intelligence

What's hot (20)

PDF
Hpe partner summit proposal 2017
PDF
Vertex perspectives ai optimized chipsets (part i)
PDF
Deep learning customer stories
PPTX
Algorithm Marketplace and the new "Algorithm Economy"
PPTX
Edge Computing & AI
PPTX
byteLAKE's Alveo FPGA Solutions
PDF
Vertex Perspectives | AI Optimized Chipsets | Part IV
PPT
Dubai Airport 2012 Data Center Strategies
PDF
FIWARE Global Summit - Standard Data Models for the Integration of FIWARE and...
PDF
Cheryl Wiebe - Advanced Analytics in the Industrial World
PDF
Internet of Things introduction
PPTX
AI Microservices APIs and Business Automation as a Service Denis Gagne
PDF
Vertex Perspectives | AI Optimized Chipsets | Part II
PDF
Er vi alle vindere når ny teknologi, -arbejdsformer og innovation udfordrer d...
PDF
EDGE COMPUTING
PPTX
AI as a Catalyst for IoT
PDF
"Data Annotation at Scale: Pitfalls and Solutions," a Presentation from Intel
PDF
NUS-ISS Learning Day 2018- Harnessing the power of cloud solutions in urban a...
PDF
Introduction to IoT Architecture
PDF
Cognitive Digital Twin by Fariz Saračević
Hpe partner summit proposal 2017
Vertex perspectives ai optimized chipsets (part i)
Deep learning customer stories
Algorithm Marketplace and the new "Algorithm Economy"
Edge Computing & AI
byteLAKE's Alveo FPGA Solutions
Vertex Perspectives | AI Optimized Chipsets | Part IV
Dubai Airport 2012 Data Center Strategies
FIWARE Global Summit - Standard Data Models for the Integration of FIWARE and...
Cheryl Wiebe - Advanced Analytics in the Industrial World
Internet of Things introduction
AI Microservices APIs and Business Automation as a Service Denis Gagne
Vertex Perspectives | AI Optimized Chipsets | Part II
Er vi alle vindere når ny teknologi, -arbejdsformer og innovation udfordrer d...
EDGE COMPUTING
AI as a Catalyst for IoT
"Data Annotation at Scale: Pitfalls and Solutions," a Presentation from Intel
NUS-ISS Learning Day 2018- Harnessing the power of cloud solutions in urban a...
Introduction to IoT Architecture
Cognitive Digital Twin by Fariz Saračević
Ad

Viewers also liked (8)

PPTX
Arpan pal u world2012
PPTX
Arpan pal besu
PPT
Contest presentation ocr
PPT
Bitm2003 802.11g
PPTX
Arpan pal tac tics2012
PPTX
Arpan pal ncccs
PDF
Vinum master wine list sept 13 Original
PPTX
Keys to a Successful Nonprofit Brand
Arpan pal u world2012
Arpan pal besu
Contest presentation ocr
Bitm2003 802.11g
Arpan pal tac tics2012
Arpan pal ncccs
Vinum master wine list sept 13 Original
Keys to a Successful Nonprofit Brand
Ad

Similar to Arpan pal gridcomputing_iot_uworld2013 (20)

PPTX
Grid computing iot_sci_bbsr
PPTX
Grid computing iot_sci_bbsr
PDF
IRJET - Importance of Edge Computing and Cloud Computing in IoT Technolog...
PPTX
Arpan pal icdcn
PDF
IRJET- Enabling Distributed Intelligence Assisted Future Internet of thing Co...
PDF
Deep Learning Approaches for Information Centric Network and Internet of Things
PDF
3581759.pdfdfdsfdsfdsfdsfdsffdsfdsfdsfdsfdsfds
PPTX
Edge Computing.pptx
PDF
Edge computing and its role in architecting IoT
PDF
Introduction to Internet of Things (IoT)
PDF
Computing At The Edge New Challenges For Service Provision Georgios Karakonst...
PDF
Computing At The Edge New Challenges For Service Provision Georgios Karakonst...
PDF
Edge Computing.pdf
PPTX
Mastering IoT Design: Sense, Process, Connect: Processing: Turning IoT Data i...
PPTX
Edge Comp.pptx
PPTX
Edge Comp.pptx
PPTX
Edge comp
PDF
20180115 Mobile AIoT Networking-ftsai
PPTX
What is Edge Computing and Why does it matter in IoT?
PDF
Future Connected Technologies Growing Convergence And Security Implications M...
Grid computing iot_sci_bbsr
Grid computing iot_sci_bbsr
IRJET - Importance of Edge Computing and Cloud Computing in IoT Technolog...
Arpan pal icdcn
IRJET- Enabling Distributed Intelligence Assisted Future Internet of thing Co...
Deep Learning Approaches for Information Centric Network and Internet of Things
3581759.pdfdfdsfdsfdsfdsfdsffdsfdsfdsfdsfdsfds
Edge Computing.pptx
Edge computing and its role in architecting IoT
Introduction to Internet of Things (IoT)
Computing At The Edge New Challenges For Service Provision Georgios Karakonst...
Computing At The Edge New Challenges For Service Provision Georgios Karakonst...
Edge Computing.pdf
Mastering IoT Design: Sense, Process, Connect: Processing: Turning IoT Data i...
Edge Comp.pptx
Edge Comp.pptx
Edge comp
20180115 Mobile AIoT Networking-ftsai
What is Edge Computing and Why does it matter in IoT?
Future Connected Technologies Growing Convergence And Security Implications M...

More from Arpan Pal (20)

PPTX
Mobisys io t_health_arpanpal
PPTX
Tcs tele rehab-hod-0.4
PPTX
Io t standard_bis_arpanpal
PPTX
Healthcare arpan pal gws
PPTX
Io t of actuating things
PPTX
Arpan pal u-world
PPTX
Arpan pal csi2012
PPT
Contest presentation epg
PPT
Embedded
PPT
Euro india2006 wirelessradioembeddedchallenges
PPT
Generic mac
PPT
Heig tcs
PPT
Hip case study tcs iitb
PPT
Icst 2012 pres
PPTX
Intelligent infra arpan pal_bit
PPTX
Io t of actuating things
PPT
Tidc 2007 healthcare
PPT
Isce logo detection_tcs
PPT
Isce osk tcs
PPTX
I tac tics_ntelligent infra_r&d
Mobisys io t_health_arpanpal
Tcs tele rehab-hod-0.4
Io t standard_bis_arpanpal
Healthcare arpan pal gws
Io t of actuating things
Arpan pal u-world
Arpan pal csi2012
Contest presentation epg
Embedded
Euro india2006 wirelessradioembeddedchallenges
Generic mac
Heig tcs
Hip case study tcs iitb
Icst 2012 pres
Intelligent infra arpan pal_bit
Io t of actuating things
Tidc 2007 healthcare
Isce logo detection_tcs
Isce osk tcs
I tac tics_ntelligent infra_r&d

Recently uploaded (20)

PDF
Bridging biosciences and deep learning for revolutionary discoveries: a compr...
PPTX
KOM of Painting work and Equipment Insulation REV00 update 25-dec.pptx
PPTX
PA Analog/Digital System: The Backbone of Modern Surveillance and Communication
PDF
Reach Out and Touch Someone: Haptics and Empathic Computing
PDF
NewMind AI Weekly Chronicles - August'25 Week I
PDF
Review of recent advances in non-invasive hemoglobin estimation
PDF
Peak of Data & AI Encore- AI for Metadata and Smarter Workflows
PDF
Unlocking AI with Model Context Protocol (MCP)
DOCX
The AUB Centre for AI in Media Proposal.docx
PDF
Encapsulation_ Review paper, used for researhc scholars
PDF
Architecting across the Boundaries of two Complex Domains - Healthcare & Tech...
PDF
Encapsulation theory and applications.pdf
PPTX
Detection-First SIEM: Rule Types, Dashboards, and Threat-Informed Strategy
PPTX
Effective Security Operations Center (SOC) A Modern, Strategic, and Threat-In...
PPT
Teaching material agriculture food technology
PPTX
A Presentation on Artificial Intelligence
PPTX
Digital-Transformation-Roadmap-for-Companies.pptx
PDF
Approach and Philosophy of On baking technology
PDF
Agricultural_Statistics_at_a_Glance_2022_0.pdf
PDF
Spectral efficient network and resource selection model in 5G networks
Bridging biosciences and deep learning for revolutionary discoveries: a compr...
KOM of Painting work and Equipment Insulation REV00 update 25-dec.pptx
PA Analog/Digital System: The Backbone of Modern Surveillance and Communication
Reach Out and Touch Someone: Haptics and Empathic Computing
NewMind AI Weekly Chronicles - August'25 Week I
Review of recent advances in non-invasive hemoglobin estimation
Peak of Data & AI Encore- AI for Metadata and Smarter Workflows
Unlocking AI with Model Context Protocol (MCP)
The AUB Centre for AI in Media Proposal.docx
Encapsulation_ Review paper, used for researhc scholars
Architecting across the Boundaries of two Complex Domains - Healthcare & Tech...
Encapsulation theory and applications.pdf
Detection-First SIEM: Rule Types, Dashboards, and Threat-Informed Strategy
Effective Security Operations Center (SOC) A Modern, Strategic, and Threat-In...
Teaching material agriculture food technology
A Presentation on Artificial Intelligence
Digital-Transformation-Roadmap-for-Companies.pptx
Approach and Philosophy of On baking technology
Agricultural_Statistics_at_a_Glance_2022_0.pdf
Spectral efficient network and resource selection model in 5G networks

Arpan pal gridcomputing_iot_uworld2013

  • 1. 0Copyright © 2013 Tata Consultancy Services Limited u-World 2013, 22nd June 2013 Distributed Edge-Computing for Internet- of-Things Arpan Pal Principal Scientist and Research Head Innovation Lab, Kolkata Tata Consultancy Services With Arijit Mukherjee and Soma Bandyopadhyay Innovation Lab, Kolkata
  • 2. Outline Analytics in Internet of Things Requirements and Challenges Challenges and Solution Approach Innovation@TCS
  • 4. 3 Signal Processing Internet-of-Things - towards Intelligent Infrastructure Sense Extract Analyze Respond Learn Monitor Intelligent Infra @Home @Building @Vehicle @Utility @Mobile @Store @Road “Intelligent” (Cyber) “Infrastructure” (Physical) APPLICATION SERVICES BACK-END PLATFORM INTERNET GATEWAY Sense Extract Analyze Respond Communication Computing
  • 5. 4 IoT Platform from TCS Internet End Users Administrators Device Integration & Management Services Analytics Services Application Services Storage Messaging & Event Distribution Services ApplicationServices Presentation Services Application Support Services Middleware Edge Gateway Sensors Internet Back-end on Cloud RIPSAC – Real-time Integrated Platform for Services & AnalytiCs Traditional Internet  Service Delivery Platform & App Development Platform  Security/Privacy Framework  Lightweight M2M Protocols  Analytics-as-a- Service  Social Network Integration  SDKs and APIs for App developer Grid Computing Components
  • 6. 5 Utility Appliances Smart Plugs Intelligent Gateway Smart Meter Demand Forecasting Demand Response Appliance Management Consumption View Appliance Scheduling On-off Control Social Network Integration Consumer Home Analytics Home Energy Management RIPSAC
  • 7. 6 Healthcare – Remote Medical Consultation ECG Body Fat Analyzer Blood Pressure Monitor Pulse OxyMeter Mobile gateway Patient Records Health Center / Home Expert Doctor Analytics and Decision Support Systems Wireless gateway
  • 8. 7 Communication & Reporting Forecast 1 Forecast 2 Adaptive Combination Forecast 3 . . Cloud Services for Adaptive Wind Forecasting Protocol Convertor SCADA Workstation 2 SCADA Workstation 1 Wind Operator Control Room Internet TCS ReSolver ABG Model on RIPSAC •Adaptive forecast •Program maintenance •Reporting
  • 10. 9 Grid Computing and IoT  It is all about Intelligent Systems  Intelligence comes from Analytics  Need for crunching huge amount of sensor data and respond in real-time  Needs huge computing infrastructure in cloud  Another option is to distribute computing load to the edge devices
  • 11. 10 The Grid in IoT is in the Edge - Fog Computing • Flavio Bonomi et.al. MCC2012, Helsinki, Finland
  • 12. 11 Advantages  Edge Devices computing power remain unused most of the time o Free Computing resource for the grid o Potentially millions of ~1GHz Processors on the grid depending upon use case  Energy cost at edge is typically at consumer rates << Energy cost at cloud which is at Enterprise rates o Energy cost account for 50% of Data Center Opex
  • 13. 12 Challenges • Communication and Energy Cost incurred at Edge • How to reduce the cost of Communication • How to preserve the Battery power • Should not effect the user experience during normal usage • How to sense idle time in real-time and allocate job / distribute data optimally • Smartphones as edge devices • Incentivisation for users to allow this • Edge devices are typically constrained in memory and have variety of hardware and software flavors • Need to factor in device capability in job scheduling design • Need to create common middleware framework for job distribution / execution
  • 15. 14 Solution Approach • Agent-based grid Computing using CONDOR • Need for agents in diverse types of edge devices via a common framework • Min-Jen Tsai, ,Yuan-Fu Luo , Expert Systems with Applications, Volume 36, Issue 7, Sept. 2009, Elsevier
  • 16. 15 Framework for Distributed Computing in IoT
  • 17. 16 Communication Aspect- Replace HTTP • http://people.inf.ethz.ch/mkovatsc/californium.php • Ralf Koetter, Muriel Medard, 2003 IEEE/ACM transaction http://web.mit.edu/medard/www/NWCFINAL.pdf • Bandyopadhyay, S. and Bhattacharyya, A. Lightweight Internet protocols for web enablement of sensors using constrained gateway devices. In Proc. International Conference on Computing, Networking and Communications (ICNC), 2013, San Diego, CA, IEEE(2013), 334 – 340 Use suitable lightweight application protocol between edge devices and core network
  • 18. 17 Computation Aspect The Wind Turbine Problem  N predictors  Computation (in R) takes 10 min for each predictor  Prediction cycle starts every 30 mins  Current solution uses HA Proxy to schedule jobs to Rserve instances.
  • 19. 18 Inferences  CPU utilization better in Condor  Turn-around time are almost equivalent  Condor starts performing better with more nodes  Further advantages in Condor w.r.t – Heterogeneity – Versatility – Matchmaking & scheduling Computation Aspect – Need for a Scheduler Scheduler is Important
  • 21. 20 Tata Consultancy Services Ltd. (TCS)  Pioneer & Leader in Indian IT TCS was established in 1968  One of the top ranked global software service provider  Largest Software service provider in Asia  250,000+ associates  USD 10B + annual revenue  Global presence  First Software R&D Center in India - 20 -
  • 22. 21 Innovation@TCS - Innovation Labs Bangalore, India1 TCS Innovation Labs - Bangalore Chennai, India2 TCS Innovation Labs - Chennai TCS Innovation Labs - Retail TCS Innovation Labs - Travel & Hospitality TCS Innovation Labs - Insurance TCS Innovation Labs - Web 2.0 TCS Innovation Labs - Telecom Cincinnati, USA3 TCS Innovation Labs - Cincinnati Delhi, India4 TCS Innovation Labs - Delhi Hyderabad, India5 TCS Innovation Labs - Hyderabad TCS Innovation Labs - CMC Kolkata, India6 TCS Innovation Labs - Kolkata Mumbai, India7 TCS Innovation Labs - Mumbai TCS Innovation Labs - Performance Engineering Peterborough, UK8 TCS Innovation Labs - Peterborough Pune, India9 TCS Innovation Labs - TRDDC - Process Engineering TCS Innovation Labs - TRDDC - Software Engineering TCS Innovation Labs - TRDDC - Systems Research TCS Innovation Labs - Engineering & Industrial Services 1 2 3 4 5 97 6 8 2000+ Associates in Research, Development and Asset Creation 19 Innovation Labs