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 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
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
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
260,000+ associates
USD 11B + annual revenue
Global presence
First Software R&D Center in India
- 20 -
24. 23
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
