1. Advanced service-based data analytics:
concepts and designs
Hong-Linh Truong
Distributed Systems Group,
Vienna University of Technology
truong@dsg.tuwien.ac.at
dsg.tuwien.ac.at/staff/truong
1ASE Summer 2014
Advanced Services Engineering,
Summer 2014, Lecture 7
Advanced Services Engineering,
Summer 2014, Lecture 7

2. Outline
 Principles of elasticity for advanced service-
based data analytics
 Data analytics within a single system
 Data analytics across multiple systems
 Composable cost evaluation
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3. PRINCIPLES OF ELASTICITY FOR DATA
ANALYTICS
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4. Advanced service-based data
analytics (1)
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Cities, e.g. including:
10000+ buildings
1000000+ sensors
Near
realtime
analytics
Near
realtime
analytics
Predictive
data
analytics
Visual
Analytics
Enterprise
Resource
Planning
Enterprise
Resource
Planning
Emergency
Management
Emergency
Management
Internet/public cloud
boundary
Organization-specific
boundary
Tracking/Log
istics
Tracking/Log
istics
Infrastructure
Monitoring
Infrastructure
Monitoring
Infrastructure/Internet of Things
......

5. Advanced service-based data
analytics (2)
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A lot of input data (L0):
~2.7 TB per day
A lot of results (L1, L2):
e.g., L1 has ~140 MB per
day for a grid of
1kmx1km
Soil
moisture
analysis for
Sentinel-1
Michael Hornacek,Wolfgang Wagner, Daniel Sabel, Hong-Linh Truong, Paul Snoeij, Thomas Hahmann, Erhard Diedrich, Marcela Doubkova,
Potential for High Resolution Systematic Global Surface Soil Moisture Retrieval Via Change Detection Using Sentinel-1, IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensing, April, 2012
Michael Hornacek,Wolfgang Wagner, Daniel Sabel, Hong-Linh Truong, Paul Snoeij, Thomas Hahmann, Erhard Diedrich, Marcela Doubkova,
Potential for High Resolution Systematic Global Surface Soil Moisture Retrieval Via Change Detection Using Sentinel-1, IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensing, April, 2012
Data-as-a-Service
and Platform-as-a-
Service in clouds
Data-as-a-Service
and Platform-as-a-
Service in clouds

6. Advanced service-based data
analytics -- fundamental concepts
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Part A Part B ...... Part N
Cluster Grid Local Cloud Public cloud/Sky
Applications
System
infrastructures
Domain 1 Domain 2 Domain n

7. Design questions
 Which system infrastructures are used?
 Which are the functions of units?
 Which interfaces are suitable for units?
 Which programming models are used within units?
 Which are fundamental units to be used?
 How do different units interact?
 Which non-functional parameters are important and
how to measure them?
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Part = a (composite) service unitPart = a (composite) service unit

8. Fundamental concepts – system
infrastructure unit
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System
infrastructures
Cloud
Software-
based Cloud
system
Human-based
Cloud system
Cluster Grid
High
Performance
Server

9. Fundamental concepts – unit
functions
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Function
Front-
end/Presentation
Data Analytics
Service
Visualization Service Middleware
Enterprise Service
Bus
Publish/Subscription Messaging/Queuing Data Transfer

10. Fundamental concepts –
programming model within units
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Programming
model
MapReduce MPI
Parallel
Database
Workflow
Other
solutions

11. Fundamental concepts – interfaces
between units
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Interface
Standard
REST SOAP
APIs
Specific APIs
Standard APIs
(e.g.
OpenStack)
Interaction
Pull Push

12. Fundamental concepts – services
and data concerns
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Service and
data
concerns
Data
concerns
Quality of
data
Pricing Data Right ...
Service
Concerns
QoS Pricing ...

13. Complex dependencies in (big)
data analytics
 More data  more
computational resources
(e.g. more VMs)
 More types of data 
more computational models
 more analytics
processes
 Change quality of results
 Change quality of data
 Change response time
 Change cost
 Change types of result
(form of the data
output, e.g. tree, visual,
story, etc.)
 More data  more
computational resources
(e.g. more VMs)
 More types of data 
more computational models
 more analytics
processes
 Change quality of results
 Change quality of data
 Change response time
 Change cost
 Change types of result
(form of the data
output, e.g. tree, visual,
story, etc.)
Data
Computational
Model
Analytics
Process
Analytics Result
Data
Data
DataxDatax
DatayDatay
DatazDataz
Computational
Model
Computational
ModelComputational
Model
Computational
ModelComputational
Model
Computational
Model
Analytics
Process
Analytics
ProcessAnalytics
Process
Analytics
ProcessAnalytics
Process
Analytics
Process
Quality of
Result
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Hong-Linh Truong, Schahram Dustdar, "Principles of
Software-defined Elastic Systems for Big Data
Analytics", (c) IEEE Computer Society, IEEE
International Workshop on Software Defined
Systems, 2014 IEEE International Conference on
Cloud Engineering (IC2E 2014), Boston,
Massachusetts, USA, 10-14 March 2014
Hong-Linh Truong, Schahram Dustdar, "Principles of
Software-defined Elastic Systems for Big Data
Analytics", (c) IEEE Computer Society, IEEE
International Workshop on Software Defined
Systems, 2014 IEEE International Conference on
Cloud Engineering (IC2E 2014), Boston,
Massachusetts, USA, 10-14 March 2014

14. Complex dependencies in (big)
data analytics
Elasticity principles
can be used to
support this!
Elasticity principles
can be used to
support this!
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15. Elasticity Principles: Elasticity of
data and computational models
 Multiple types of objects from different sources with
complex dependencies, relevancies, and quality
 Different data and computational models the same
analytics subject
 New analytics subjects can be defined and analytics
goals can be changed
 Decide/select/define/compose not only computational
models for analytics subjects but also data models
based on existing ones
Management and modeling of elasticity of data and computational
model during the analytics
Management and modeling of elasticity of data and computational
model during the analytics
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16. Elasticity Principles: Elasticity of
data resources
 Data provided, managed and shared by different
providers
 Data associated with different concerns (cost, quality
of data, privacy, contract, etc.
 Static data, open data, data-as-a-service, opportunistic
data (from sensors and human sensing)
 Not just centralized big data and total data ownership
Data resources can be taken into account in an elastic
manger: similar to VMs, based on their quality,
relevancy, pricing, etc.
Data resources can be taken into account in an elastic
manger: similar to VMs, based on their quality,
relevancy, pricing, etc.
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17. Elasticity Principles: Elasticity of
humans and software as computing
units
 Human in the loop to solve analytics tasks that software
cannot solve
 Human-based compute units can be scaled up/down
with different cost, availability, performance models
 Human-based compute units + software-based
compute units for executing computational models
 Elasticity controls can be also done by humans
Provisioning hybrid compute units in an elastic way for
computational/data/network tasks as well as for
monitoring/control tasks in the analytics process
Provisioning hybrid compute units in an elastic way for
computational/data/network tasks as well as for
monitoring/control tasks in the analytics process
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18. Elasticity Principles: Elasticity of
quality of results
 Definition of quality of results
 Trade-offs of time, cost, quality of data, forms of
output
 Using quality of results to select suitable
computational models, data resources,
computing units
 Multi-level control for the elasticity based on
quality of results
Able to cope with changes in quality of data,
performance, cost and types of results at runtime
Able to cope with changes in quality of data,
performance, cost and types of results at runtime
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19. WE NEED TO START FROM
DATA ANALYTICS WITHIN A
SINGLE SYSTEM
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20. Domain ADomain A
Data analytics within a single
system
 They are complex enough but
do not meet all requirements
 In a single domain
 Tightly coupled computing
infrastructures
 E.g., in the same
cloud
 Computation and data are
close
 Several concerns can be
by-passed
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Data
service
unit
Data Analytics
Unit
Not always provisioned under the „Service Unit“ model

21. Data analytics within a single
system
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1. Andrew Pavlo, Erik Paulson, Alexander Rasin, Daniel J. Abadi, David J. DeWitt, Samuel Madden, and Michael
Stonebraker. 2009. A comparison of approaches to large-scale data analysis. In Proceedings of the 2009 ACM
SIGMOD International Conference on Management of data (SIGMOD '09), Carsten Binnig and Benoit Dageville
(Eds.). ACM, New York, NY, USA, 165-178. DOI=10.1145/1559845.1559865
http://doi.acm.org/10.1145/1559845.1559865
2. Leonardo Neumeyer, Bruce Robbins, Anish Nair, Anand Kesari: S4: Distributed Stream Computing Platform. ICDM
Workshops 2010: 170-177
3. Jerry Chou, Mark Howison, Brian Austin, Kesheng Wu, Ji Qiang, E. Wes Bethel, Arie Shoshani, Oliver Rübel,
Prabhat, and Rob D. Ryne. 2011. Parallel index and query for large scale data analysis. In Proceedings of 2011
International Conference for High Performance Computing, Networking, Storage and Analysis (SC '11). ACM, New
York, NY, USA, , Article 30 , 11 pages. DOI=10.1145/2063384.2063424 http://doi.acm.org/10.1145/2063384.2063424
4. Boduo Li, Edward Mazur, Yanlei Diao, Andrew McGregor, Prashant J. Shenoy: A platform for scalable one-pass
analytics using MapReduce. SIGMOD Conference 2011: 985-996
5. Fabrizio Marozzo, Domenico Talia, Paolo Trunfio: A Cloud Framework for Parameter Sweeping Data Mining
Applications. CloudCom 2011: 367-374
6. Yingyi Bu, Bill Howe, Magdalena Balazinska, Michael D. Ernst: HaLoop: Efficient Iterative Data Processing on Large
Clusters. PVLDB 3(1): 285-296 (2010)
1. Andrew Pavlo, Erik Paulson, Alexander Rasin, Daniel J. Abadi, David J. DeWitt, Samuel Madden, and Michael
Stonebraker. 2009. A comparison of approaches to large-scale data analysis. In Proceedings of the 2009 ACM
SIGMOD International Conference on Management of data (SIGMOD '09), Carsten Binnig and Benoit Dageville
(Eds.). ACM, New York, NY, USA, 165-178. DOI=10.1145/1559845.1559865
http://doi.acm.org/10.1145/1559845.1559865
2. Leonardo Neumeyer, Bruce Robbins, Anish Nair, Anand Kesari: S4: Distributed Stream Computing Platform. ICDM
Workshops 2010: 170-177
3. Jerry Chou, Mark Howison, Brian Austin, Kesheng Wu, Ji Qiang, E. Wes Bethel, Arie Shoshani, Oliver Rübel,
Prabhat, and Rob D. Ryne. 2011. Parallel index and query for large scale data analysis. In Proceedings of 2011
International Conference for High Performance Computing, Networking, Storage and Analysis (SC '11). ACM, New
York, NY, USA, , Article 30 , 11 pages. DOI=10.1145/2063384.2063424 http://doi.acm.org/10.1145/2063384.2063424
4. Boduo Li, Edward Mazur, Yanlei Diao, Andrew McGregor, Prashant J. Shenoy: A platform for scalable one-pass
analytics using MapReduce. SIGMOD Conference 2011: 985-996
5. Fabrizio Marozzo, Domenico Talia, Paolo Trunfio: A Cloud Framework for Parameter Sweeping Data Mining
Applications. CloudCom 2011: 367-374
6. Yingyi Bu, Bill Howe, Magdalena Balazinska, Michael D. Ernst: HaLoop: Efficient Iterative Data Processing on Large
Clusters. PVLDB 3(1): 285-296 (2010)
Some papers

22. Data analytics within a single
system – some examples
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Message Passing
Interface (MPI) + Cluster-
based File system
Message Passing
Interface (MPI) + Cluster-
based File system
MapReduce + Google
File System
MapReduce + Google
File System
Hadoop + HDFSHadoop + HDFS
Dryad+LINQDryad+LINQ
Parallel Database
(SQL/NonSQL)
Parallel Database
(SQL/NonSQL)
Yahoo S4Yahoo S4
WorkflowWorkflow
A short, good overview in Chapter 6: Cloud Programming and Software Environments, Book: Distributed and Cloud
Computing – from Parallel Processing to the Internet of Things, Kai Hwang, Geoffrey C. Fox and Jack J Dongarra,
Morgan Kaufmann, 2012
A short, good overview in Chapter 6: Cloud Programming and Software Environments, Book: Distributed and Cloud
Computing – from Parallel Processing to the Internet of Things, Kai Hwang, Geoffrey C. Fox and Jack J Dongarra,
Morgan Kaufmann, 2012

23. Discussion time
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WHY ANALYTICS UNITS SHOULD BE
„CLOSED“ TO DATA UNITS?
WHICH CONCERNS COULD BE IGNORE IN
SINGLE SYSTEM DATA ANALYTICS?
WHICH ISSUES WE NEED TO CONSIDER
WHEN OUR DATA UNITS ARE IN
DIFFERENT SYSTEMS?

24. Data analytics across multiple
systems – design choice
 Programming models
for data analytics
service
 Data service units
 Supporting middleware
units
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Programming model
System
Infrastrucure
Interface

25. Data analytics across multiple systems
– programming models (1)
Static data
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Local
input
data
Analytics
Results
MapReduce/Hadoop
Workflow
MPI
Other solutions
Servers/Cloud/Cluster
What are our design concerns?What are our design concerns?
Input
data
Output
data

26. Stockmarket
Social media
M2M
Stockmarket
Social media
M2M
Data analytics across multiple systems
– programming models (2)
Near-realtime data
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Analytics
Results
Complex event processing
Stream data analysis
Other solutions
Servers/Cloud/Cluster
Input
data
Output
data
What are our design concerns?What are our design concerns?

27. Big data (e.g.,
satellite images)
Big data (e.g.,
satellite images)
Data analytics across multiple systems
– programming models (3)
Near-realtime data
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Analytics
Results
MPI
Workflow
Other solutions
Servers/Cloud/Cluster
Input
data
Output
data
What are our design concerns?What are our design concerns?

28. Data analytics across multiple
systems – data service units
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Cluster file
LustreLustreNFSNFS
Data
Analytics Unit
• Read/write data via direct ,
low-level read/write via IO
Interface
• Cluster or cluster of clusters
• Can be very large
System
• Usually parallel processing
Programming model
Hadoop File SystemHadoop File System Google file systemGoogle file system
Read/write data

29. Data analytics across multiple
systems – data service units
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Storage-as-a-
Service
Google Storage Service
(REST API)
Google Storage Service
(REST API)
Amazon S3
(SOAP/REST API)
Amazon S3
(SOAP/REST API)
Data
Analytics Unit • Direct data transfer via REST/SOAP APIs
Interface
• Decouple between analytics and storage
System
• May require middleware for data transfer
• Request via SOAP/REST
• Real data transfer done by external
middleware
• A rich set of programming models can be used
Programming model
commands data

30. Data analytics across multiple
systems – data service units
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Database-as-a-
Service
SkySQL
Amazon RDS
Microsoft SQL Azure
Clustrix DBaaS
SkySQL
Amazon RDS
Microsoft SQL Azure
Clustrix DBaaS
MongoDB/MongoLab
Amazon DynamoDB
Amazon SimpleDB
Cloudant Data
MongoDB/MongoLab
Amazon DynamoDB
Amazon SimpleDB
Cloudant Data
Data
Analytics Unit
• REST/SOAP APIs
• Mainly for commands and results
Interface
• Decouple between analytics unit and database
• Database as a sevice can be very large
System
• Analytics can be done at both sides
• Analytic units can use any programming models
• Database-as-a-service can perform a lot of
analytics
• Parallel database operations
Programming model
Technology
queries data

31. Data analytics across multiple
systems – data service units
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DaaS
Infochimps
Microsoft Azure
Xively
GNIP
Infochimps
Microsoft Azure
Xively
GNIP
Data
Analytics Unit
• Data transfer can be uni or bi-
direction
• REST/SOAP APIs
Interface
• Both systems for DaaS and for
analytics units can be very large
System
• Can be any
Programming model
Technology

32. Middleware service unit for
transfering large data -- GlobusOnline
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Source: Bryce Allen, John Bresnahan, Lisa Childers, Ian Foster, Gopi Kandaswamy, Raj Kettimuthu, Jack Kordas, Mike
Link, Stuart Martin, Karl Pickett, and Steven Tuecke. 2012. Software as a service for data scientists. Commun. ACM 55,
2 (February 2012), 81-88. DOI=10.1145/2076450.2076468 http://doi.acm.org/10.1145/2076450.2076468
Source: Bryce Allen, John Bresnahan, Lisa Childers, Ian Foster, Gopi Kandaswamy, Raj Kettimuthu, Jack Kordas, Mike
Link, Stuart Martin, Karl Pickett, and Steven Tuecke. 2012. Software as a service for data scientists. Commun. ACM 55,
2 (February 2012), 81-88. DOI=10.1145/2076450.2076468 http://doi.acm.org/10.1145/2076450.2076468

33. Middleware service unit for
transfering large data -- ProxyWS
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Spiros Koulouzis, Reginald Cushing, K. A. Karasavvas, Adam Belloum, Marian Bubak: Enabling Web Services to
Consume and Produce Large Datasets. IEEE Internet Computing 16(1): 52-60 (2012)
Spiros Koulouzis, Reginald Cushing, K. A. Karasavvas, Adam Belloum, Marian Bubak: Enabling Web Services to
Consume and Produce Large Datasets. IEEE Internet Computing 16(1): 52-60 (2012)

34. Middleware service units for
messages/queuing
 Advanced Message Queuing Protocol (AMQP)
 Simple (or Streaming) Text Orientated
Messaging Protocol (STOMP)
 Specific protocols/APIs
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Amazon SQSAmazon SQSStormMQStormMQ RabbitMQRabbitMQ

35. SOME EXAMPLES OF
COMPLEX DATA ANALYTICS
SERVICE
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36. The SMAD distributed processing
architecture
36ASE Summer 2014

37. Different possibilities: Grids and/or
clouds
 Raw images stored in archival: iRODS, HTTP server, or
Amazon S3
 Notification
 Any queuing system: on-premise or cloud-based
service
 Reference images:
 Local/pre-deployed or deployed on demand
 Computation: set of workstations, cluster, EC2, etc.
 Sentinel-1 images and SSM storage:
 Local files, cloud storage, iRODs, etc.
 Result notification and sharing: to whom? At which
scale?
37
The choices are also strongly dependent on “collaboration
needs” and money! But how easy data sharing is?
The choices are also strongly dependent on “collaboration
needs” and money! But how easy data sharing is?
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38. Prototype
38
PBS on Vienna Scientific
Cluster (vsc.ac.at), In total
~ 4000 cores
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39. 39
Illustrative experiment (1)
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40. Sustainability governance analysis
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Cities, e.g. including:
10000+ buildings
1000000+ sensors
Near
realtime
analytics
Near
realtime
analytics
Predictive
data
analytics
Visual
Analytics
Enterprise
Resource
Planning
Enterprise
Resource
Planning
Emergency
Management
Emergency
Management
Internet/public cloud
boundary
Organization-specific
boundary
Tracking/Log
istics
Tracking/Log
istics
Infrastructure
Monitoring
Infrastructure
Monitoring
Infrastructure/Internet of Things
......

41. DaaS for sustainability governance
 Monitoring data DaaS
 Domain-specific knowledge DaaS
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Hong-Linh Truong, Schahram Dustdar , Sustainability Data and Analytics in Cloud-Based M2M Systems, Big Data
and Internet of Things: A Roadmap for Smart Environments Studies in Computational Intelligence Volume 546, 2014, pp
343-365
Hong-Linh Truong, Schahram Dustdar , Sustainability Data and Analytics in Cloud-Based M2M Systems, Big Data
and Internet of Things: A Roadmap for Smart Environments Studies in Computational Intelligence Volume 546, 2014, pp
343-365

42. Platform-as-a-Service for
Sustainability Governance
• different types of analytics application models,
such as batch, workflow and stream applications
and intelligent bots
 different programming models and languages
 For analytics of large-scale data but also bot-as-
a-service
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43. Cloud-based Sustainability
governance analysis framework
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44. Cloud-based Sustainability
governance analysis framework
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45. HOW TO DEAL WITH COST
AND QUALITY OF COMPLEX
SERVICES?
Discussion time
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46. 46 46
Examples of our complex data
analytics
 Bio-mechanic applications
 Simulate the stiffness of human bones
 Data and computation intensive applications
 Sequential and parallel programs (e.g., parfe and paraview),
 Complex software installation: Parmetis, Trilinos, Parfe, Paraview, and HDF5
 run under batch and interactive modes
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47. Composable evaluation approach
 We test with „cost“
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Part A Part B ...... Part N

48. Dealing with performance and cost
of complex applications in clouds
 Application complexity
 Elastic high performance applications on multiple clouds: libraries, software
services, virtual machines, etc.
 Cost and performance are needed for determining which parts of the application
should be excuted in the clouds and when
 Cost/performance model complexity
 Coarse- and fine-grained cost models of clouds at different layers:
 Too coarse-grained (networks, storages, machines) or too fine-grained (IO
calls)
 Software-, data-, human-specific cost/performance models
 Cost models for individual parts (workflow, MPI, OpenMP, etc.)
Tran Vu Pham, Hong-Linh Truong, Schahram Dustdar "Elastic High Performance Applications - A Composition
Framework", The 2011 Asia-Pacific Services Computing Conference (IEEE APSCC 2011), (c) IEEE Computer Society,
December 12 - 15, 2011, Jeju, Korea
Hong Linh Truong, Schahram Dustdar: Composable cost estimation and monitoring for computational applications in
cloud computing environments. Procedia CS 1(1): 2175-2184 (2010)
Tran Vu Pham, Hong-Linh Truong, Schahram Dustdar "Elastic High Performance Applications - A Composition
Framework", The 2011 Asia-Pacific Services Computing Conference (IEEE APSCC 2011), (c) IEEE Computer Society,
December 12 - 15, 2011, Jeju, Korea
Hong Linh Truong, Schahram Dustdar: Composable cost estimation and monitoring for computational applications in
cloud computing environments. Procedia CS 1(1): 2175-2184 (2010)
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49. Composable cost evaluation
Part A Part B Part C
Cost/performance
model i
Cost/performance
model j
Cost/performance
model k
Runtime:
Elastic
processes
Elastic high performance applications on multiple clouds:
libraries, software services, virtual machines, etc.
Utilize different
performance and
dependencies models
for sequential, parallel,
workflows, etc.
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50. Composable cost evaluation:
Estimation and Monitoring
 Leverage our previous knowledge on event representations,
application monitoring, performance analysis, dependability
analysis
 Employ service-oriented approach
 RESTful service, JSON and XML event data
50 50ASE Summer 2014

51. Event Representations and
Instrumentation
 Captured monitoring events based on a well-defined
specification
– Well-known instrumentation techniques can be
reused
 Consider different application execution models (e.g.,
MPI, workflows, etc.)
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52. Composable cost evaluation --
Fine-grained composition cost
models
52 52ASE Summer 2014

53. Composable cost evaluation --
Illustrative experiments
53
Examples with the Bones application
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54. Simple Cost Estimation - Examples
 aaaa
54ASE Summer 2014 54

55. Online Cost Monitoring - Examples
 One experiment of a
bioinformatic
workflow in EC2
 Support runtime
cost-based
composition and
execution
55ASE Summer 2014 55

56. Exercises
 Read mentioned papers
 Analyze the relationships between programming
models and system infrastructures for data
analytics across multiple domains
 Examine http://cloudcomputingpatterns.org and
see how it supports data analytics patterns
 Develop some patterns for data analytics across
multiple systems
 Work on composable cost evaluation for
complex data analytics
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57. 57
Thanks for
your attention
Hong-Linh Truong
Distributed Systems Group
Vienna University of Technology
truong@dsg.tuwien.ac.at
dsg.tuwien.ac.at/staff/truong
ASE Summer 2014