arpan pal gridcomputing_iot_uworld2013
TRANSCRIPT
1 Copyright © 2013 Tata Consultancy Services Limited u-World 2013, 22nd June 2013
Distributed Edge-Computing for Internet-of-Things
Arpan PalPrincipal Scientist and Research Head
Innovation Lab, Kolkata Tata Consultancy Services
With Arijit Mukherjee and Soma BandyopadhyayInnovation Lab, Kolkata
OutlineAnalytics in Internet of Things
Requirements and Challenges
Challenges and Solution Approach
Innovation@TCS
Analytics in Internet-of-Things
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Signal
Processing
Internet-of-Things - towards Intelligent Infrastructure
Sense
Extract
Analyze
Respond
Learn
Monitor
IntelligentInfra
@Home
@Building
@Vehicle@Utility
@Mobile
@Store
@Road
“Intelligent” (Cyber) “Infrastructure” (Physical)
APPLICATION SERVICES
BACK-END PLATFORM
INTERNET
GATEWAY
Internet-of-Things (IoT) Framework
Sense
Extract
Analyze
Respond
Communication
Computing
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IoT Platform from TCS
Internet
End Users Administrators
Device Integration & Management Services
Analytics Services
Application Services
Storage
Messaging & Event Distribution Services
Ap
plic
ati
on
Serv
ices
Presentation Services
Application Support ServicesM
iddle
ware
Edge Gateway
Sensors
Internet
Back-end on Cloud
RIPSAC – Real-time Integrated Platform for Services & AnalytiCs
TraditionalInternet
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
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Utility
AppliancesSmart Plugs
IntelligentGateway
Smart Meter
Demand ForecastingDemand ResponseAppliance Management
Consumption ViewAppliance Scheduling
On-off Control
Social Network Integration
Consumer Home
Analytics
Home Energy Management
RIPSAC
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Healthcare – Remote Medical Consultation
ECG
Body Fat Analyzer
Blood PressureMonitor
Pulse OxyMeter
Healthcare
Portal
Mobile gateway
Web Request
PatientRecords
Health Center / Home
Expert Doctor
Analytics and
Decision Support Systems
Wireless gateway
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Communication & Reporting
Forecast 1
Forecast 2
Adaptive Combination
Forecast 3..
Cloud Services for Adaptive Wind Forecasting
Wind Park
Protocol Convertor
SCADAWorkstation 2
SCADAWorkstation 1
Wind Operator Control Room
Internet
•Adaptive forecast•Program maintenance
•Reporting
Requirements and Challenges
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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
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The Grid in IoT is in the Edge - Fog Computing
• Flavio Bonomi et.al. MCC2012, Helsinki, Finland
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Advantages
Edge Devices computing power remain unused most of the time
o Free Computing resource for the grido 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
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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
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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
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Framework for Distributed Computing in IoT
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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
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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.
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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
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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
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