TUW-ASE-Summer 2014: Advanced Services Engineering- Introduction
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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.
TUW-ASE-Summer 2014: Advanced Services Engineering- Introduction
- 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 ASE Summer 2014 3
- 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 ASE Summer 2014 4 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) ASE Summer 2014 5 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) ASE Summer 2014 7 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! ASE Summer 2014 8
- 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! ASE Summer 2014 9
- 10. ARE YOU WORKING ON SUCH SYSTEMS? ARE YOU CONVINCED THAT THIS COURSE IS SUITABLE FOR YOU? Questions ASE Summer 2014 10
- 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 ASE Summer 2014 11
- 12. What is the course about? (2) ASE Summer 2014 12 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 ASE Summer 2014 13
- 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 ASE Summer 2014 14
- 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 ASE Summer 2014 15
- 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 ASE Summer 2014 16
- 17. Grades Participations + discussions: 10 points Assignments: 40 points Mini project: 20 points Final oral examination: 30 points ASE Summer 2014 17 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? ASE Summer 2014 18
- 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