This document provides an introduction and outline for the Advanced Services Engineering summer course. The course will cover engineering large-scale, Internet-based service systems for applications involving big data, cloud computing, IoT, and human computation. Topics will include data provisioning and analytics across multiple cloud environments, quality-aware workflow design, and hybrid software and human-based service systems. The course is intended for master's and PhD students with backgrounds in distributed systems and computing. It will be evaluated based on assignments, a mini-project, and final oral exam.

1. Advanced Services Engineering-
Introduction
Hong-Linh Truong
Distributed Systems Group,
Vienna University of Technology
truong@dsg.tuwien.ac.at
http://dsg.tuwien.ac.at/staff/truong
1ASE Summer 2014
Advanced Services Engineering,
Summer 2014
Advanced Services Engineering,
Summer 2014

2. Outline
 Why advanced services engineering?
 What is the course about?
 Course administration
ASE Summer 2014 2

3. ASE – current trends
 Big data
 Enabling big data storages and high performance,
scalable data analytics at data centers
 Cloud and service computing models
 Facilitating dynamic and flexible data and service
provisioning/integration
 Human computation
 Enabling human-in-the-loop of computation and analytics
 IoT clouds
 Dealing with sensors/actuators and gateways integration
with cloud data centers
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4. ASE – complex requirements
 Big and near real-time data must be handled in a timely manner to extract
insightful information
 Cross-boundary, Internet-scale computational, data and network services
integration must be done
 Complex applications/sytems executed atop multiple, diverse computing
environments
 Data centers/cloud infrastructures, IoT systems, human computation
environments, etc.
 Multiple concerns wrt quality, regulation and cost/benefits must be
assured.
 Flexible and dynamic management, e.g., software-defined and elastic
capabilities
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Engineering Internet-scale service-based systems for these requirements is
very challenging
Engineering Internet-scale service-based systems for these requirements is
very challenging

5. ASE -- application examples (1)
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Equipment Operation
and Maintenance
Equipment Operation
and Maintenance
Civil protectionCivil protection
Building Operation
Optimization
Building Operation
Optimization
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
......

6. ASE – application examples
(2)
ASE Summer 2014 6
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
Data-as-a-Service
and Platform-as-a-
Service in clouds
Data-as-a-Service
and Platform-as-a-
Service in clouds

7. ASE – application examples (3)
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Source: http://www.undata-api.org/
Source:
http://www.strikeiron.com/Catalog/StrikeIronServices.aspx
Source: http://docs.gnip.com/w/page/23722723/Introduction-
to-Gnip

8. ASE – complex, diverse and elastic
properties
 Different platforms and multiple services from multiple
providers for multiple stakeholders
Complex service-based systems
Not just big data in a single organization which can be
dealt by using, e.g., MapReduce/Hadoop
Not just take the data and do the computation: how to
guarantee multitude of data/service concerns
Not just things and software: human-in-the-loop
 Quality of analytics results are elastic: they are not
fixed and dependent on specific contexts!
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9. ASE – relevant courses
 Existing courses provide foundations
 Advanced Internet Computing
 Give you some advanced technologies in Internet Computing but
not focus very much one large-scale, data intensive services
systems
 Distributed Systems
 Give you fundamental distributed system concepts and
technologies only
 Distributed Systems Technologies:
 Give you fundamental technologies and how to use them
 But they do not deal with engineering such large-scale,
complex service-based systems
 Big, near-realtime data and complex service integration are the
driving force!
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10. ARE YOU WORKING ON SUCH
SYSTEMS? ARE YOU
CONVINCED THAT THIS
COURSE IS SUITABLE FOR
YOU?
Questions
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11. What is the course about? (1)
 Discuss new concepts and techniques for
engineering advanced, Internet-scale, elastic
service-based systems
 Focus on service systems for data analytics,
elasticity capabilities, and software-defined
environments
 Consider a wide range of applications for real-
world problems in machine-to-machine (M2M),
science and engineering, and social media
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12. What is the course about? (2)
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Big/realtime
Data
Big/realtime
Data
Data
Provisioning
Data
Provisioning
Data
Analytics
Data
Analytics
Quality of data -/Quality of Result - aware workflow design and optimizationQuality of data -/Quality of Result - aware workflow design and optimization
Service engineering and integration in multiple cloud environmentsService engineering and integration in multiple cloud environments
Hybrid software-based and human-based service systems engineeringHybrid software-based and human-based service systems engineering
•Platforms
•Data concerns,
•Data concern monitoring
and evaluation
•Platforms
•Data concerns,
•Data concern monitoring
and evaluation
•Data-as-a-service
(DaaS)
•Data Marketplaces
•Data Elasticity
•Data-as-a-service
(DaaS)
•Data Marketplaces
•Data Elasticity
•Principles of big data
analytics
•Hybrid software and human-
based services
•Multi-cloud analytics
services
•Principles of big data
analytics
•Hybrid software and human-
based services
•Multi-cloud analytics
services
Focus
Topics
Science, social, business, machine-to-machine and open dataScience, social, business, machine-to-machine and open data

13. References for the course
 No text book designed for this course
 Some references from recent scientific papers
 Relevant research in big data
 But not very much on data management or single
organization data analytics (e.g.,
MapReduce/Hadoop)
 Relevant work in Internet of Things, People and
Software integration
 Distributed and Cloud Computing
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14. Course administration (1)
 Lectures are held through the whole semester
 But not every week – check the course website!
 Who could participate?
 Master students in advanced stages (e.g., seeking for
master thesis) in informatics and business informatics
 PhD students: PhD School of Informatics, Doctoral
College of Adaptive Systems
 Students should have knowledge about fundamental
distributed systems, internet computing and
distributed computing technologies
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15. Course administration (2)
 Three course segments
 Overview and understanding of complexity in
engineering Internet-scale advanced service systems
 Data issues in engineering complex services
 Lectures and assignments
 Services and service integration issues in complex
services engineering
 Lectures and a mini project
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16. Course administration (3)
 Evaluation methods
 Assignments, a mini project and a final examination
 Assignments
 4 home assignments resulting in some analysis
summaries
 Mini project
 One mini project resulting in a small
prototype/conceptual design
 Oral final exam
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17. Grades
 Participations + discussions: 10 points
 Assignments: 40 points
 Mini project: 20 points
 Final oral examination: 30 points
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Point Final mark
90-100 1 (sehr gut)
75-89 2 (gut)
56-74 3 (befriedigend)
40-55 4 (genügend)
0-39 5 (nicht genügend)

18. ANY QUESTION?
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19. 19
Thanks for
your attention
Hong-Linh Truong
Distributed Systems Group
Vienna University of Technology
truong@dsg.tuwien.ac.at
http://dsg.tuwien.ac.at/staff/truong
ASE Summer 2014