Internet of Things, IoT

1. 0Copyright © 2013 Tata Consultancy Services Limited u-World 2013, 22nd June 2013
Distributed Edge-Computing for Internet-
of-Things
Dr. Arpan Pal
Principal Scientist and Research Head
Innovation Lab, Kolkata
Tata Consultancy Services
With Arijit Mukherjee and Soma Bandyopadhyay
Innovation Lab, Kolkata

2. 1
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
 260,000+ associates
 USD 11B + annual revenue
 Global presence
 First Software R&D Center in India
- 1 -

3. 2
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

4. Outline
Analytics in Internet of Things
Requirements and Challenges
Challenges and Solution Approach

5. Analytics in Internet-of-Things

6. 5
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

7. 6
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 Services & Analytics for Cyberphysical Systems
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

8. 7
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

9. 8
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

10. 9
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

11. Requirements and Challenges

12. 11
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

13. 12
The Grid in IoT is in the Edge - Fog Computing
• Flavio Bonomi et.al. MCC2012, Helsinki, Finland

14. 13
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

15. 14
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

16. Solution Approach

17. 16
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

18. 17
Framework for Distributed Computing in IoT

19. 18
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
Optimizing Communication Overhead is Important

20. 19
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.

21. 20
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

22. Thank You
arpan.pal@tcs.com

23. 22
Why CONDOR?
• Provides a framework for running any kind of application
• Batch jobs
• Distributed tasks with dependencies, workflows (DAG)
• Executables, scripts, …
• Allows seamless scaling of infrastructure
• Flocking: Inter-operation of different condor installations
• Automatic discovery of resources matching the requirement of a job
• ClassAd : Capability announcement by resources
• Job Descriptor describes job’s requirement
• Matchmaker : resource discovery and matching
• Renowned in scientific/Grid computing world, more than 25 years of
effort