Grid computing iot_sci_bbsr
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Copyright © 2011 Tata Consultancy Services Limited CSI Eastern Regional Convention, CVRCE Bhubaneswar, 25 th Feb 2013 Grid Computing for Internet-of- Things - towards Intelligent Infrastructure Arpan Pal Principal Scientist and Research Head Innovation Lab, Kolkata Tata Consultancy Services With Arijit Mukherjee and Soma Bandyopadhyay Innovation Lab, Kolkata
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Transcript of Grid computing iot_sci_bbsr
1 Copyright © 2011 Tata Consultancy Services Limited CSI Eastern Regional Convention, CVRCE Bhubaneswar, 25th Feb
2013
Grid Computing for Internet-of-Things - towards Intelligent Infrastructure
Arpan PalPrincipal Scientist and Research Head
Innovation Lab, Kolkata Tata Consultancy Services
With Arijit Mukherjee and Soma BandyopadhyayInnovation Lab, Kolkata
OutlineInnovation@TCSUbiquity and Internet of ThingsGrid Computing for IoTExample Use CasesChallenges and Solution Approach
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Innovation@TCS - Innovation Labs
Bangalore, India1
TCS Innovation Labs - Bangalore
Chennai, India2
TCS Innovation Labs - ChennaiTCS Innovation Labs - RetailTCS Innovation Labs - Travel & HospitalityTCS Innovation Labs - InsuranceTCS Innovation Labs - Web 2.0TCS Innovation Labs - Telecom
Cincinnati, USA3
TCS Innovation Labs - Cincinnati
Delhi, India4
TCS Innovation Labs - Delhi
Hyderabad, India5
TCS Innovation Labs - HyderabadTCS Innovation Labs - CMC
Kolkata, India6
TCS Innovation Labs - Kolkata
Mumbai, India7
TCS Innovation Labs - MumbaiTCS Innovation Labs - Performance Engineering
Peterborough, UK8
TCS Innovation Labs - Peterborough
Pune, India9
TCS Innovation Labs - TRDDC - Process EngineeringTCS Innovation Labs - TRDDC - Software EngineeringTCS Innovation Labs - TRDDC - Systems ResearchTCS Innovation Labs - Engineering & Industrial Services
1 2
3
4
597
6
8
2000+
Associates in Research, Development and Asset Creation
19 Innovation Labs
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Innovation@TCS – the Co-Innovation Network
Ecosystem of innovative partners encompassing: Academic Institutions Start up companies Government Service Providers Industry Bodies
Need : A rich and diverse network that drives innovation in an open community:
Research Labs
Research Labs
Startups
End Users
Research Institutions
AcademicInstitutions
Student Community
Individuals
Service Providers
Government departments
Industry BodiesUtilities
Ubiquity and Internet of Things- towards Intelligent Infrastructure
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Ubiquitous Computing
“Ubiquitous computing enhances computer use by making many computers available throughout the physical environment, but making them effectively invisible to the user”
At present Ubiquity is viewed as a Consumer phenomenon – However widespread adoption of ubiquitous devices among Enterprise stakeholders will drive Enterprises to ubiquity.
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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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Integrated Platform for Intelligent Infrastructure
People Feedback & Emotions
Social Media
Integrated Services
Sensors & IoTPlatform
Traditional Monitoring & Control Systems Citizen Data
Smart Integration Platform
Transportation Healthcare Electricity
WaterPublic Safety Tourism
Smart Integrated Services
Sense
Analyze
Extract
Respond
Intelligence
Smart Domain Services
Community
etc.
Sense: People Activity, Appliances, Vehicles , Road, Home/Bldg, Utility Infrastructure
Detect gas leakage/water contamination : mobilize rescue team, suggest optimum route
Divert Road Traffic in case of Water Pipeline Burst
Correlate Electricity/Water /Gas consumption patterns
Intelligent Integration Platform
Integrated Intelligent Services
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Technology Landscape
Intelligent Infrastructure
Optimize
Digitize
Analyze
De-Risk
Sustain
Analytics-led transformationResource Optimization
Mobile Applications; Social Media, Digital Consumer; M2M communicationSensor Webs
Information FusionHPC, HTC, HFC and Big Data
Algorithms and Decision SciencesReal-time Response
Security; Privacy vs. Utility, Trust
Green IT; IT for Green; WaterHealthcare
Grid Computing for IoT
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The Grid
“Resource sharing & coordinated problem solving in dynamic, multi-institutional virtual organizations”
• Ian Foster, Grid Computing in Canada Workshop, University of Alberta, May 1, 2002
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Software enables: On-demand access to services Secure, reliable, dynamic federation Definition & execution of workflows
Applications: – Address complex problems– Provide community services
Facilities:– Provide access to resources– Host robust services and content
Grid Technology
• Ian Foster, Chicago Technology Forum, October 28, 2005• Min-Jen Tsai, ,Yuan-Fu Luo , Expert Systems with Applications, Volume 36, Issue 7, Sept. 2009,
Elsevier
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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
Example Use Cases
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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
Challenges and Solution Approach
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Challenges
• Using the Internet as the media for distributing data to the edge device will cost the edge device owner and use battery at edge device
• How to reduce the cost of Communication• How to preserve the Battery power
• Edge device should be used only during its idle time and should not effect the user experience during its 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
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Solution Approach – Computing Aspect
• 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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Solution Approach – Communication Aspect
HTTP is heavy-weight.
CoAP is the most efficient in terms of bandwidth.
Power usages by CoAP and HTTP to send GPS data using mobile phone as sensor gateway
Scenarios cover indoor and outdoor with increasing mobility
• http://people.inf.ethz.ch/mkovatsc/californium.php• Ralf Koetter, Muriel Medard, 2003 IEEE/ACM transaction http://web.mit.edu/medard/www/NWCFINAL.pdf• Soma Bandyopadhyay, Abhijan Bhattacharyya, Workshop on Cyber Physical Systems (CPS), 2013 International
Conference on Computing, Networking and Communication (ICNC, 2013)
COAP – the Constrained Object Access Protocol
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Broadcast Based Communication – the Future?
Universal Compaction using lossless source coding
• Farkas, P.; Halcin, F.; , "Communication techniques for wireless sensor networks using distributed universal compaction algorithms," Signal Processing and Communication Systems, 2009. ICSPCS 2009. 3rd International Conference on , vol., no., pp.1-6, 28-30 Sept. 2009
Preservation of uplink bandwidth and sensor node power
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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
