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E-grāmata: Software Architecture for Big Data and the Cloud

Edited by (Professor for software engineering, University Duisburg-Essen, Germany), Edited by (Systems Engineering Researcher/Consultant, Heidelberg, Germany), Edited by (Associate Profe), Edited by (University of Birmingham, UK), Edited by (Senior Lecturer, University of Brighton, UK)
  • Formāts: EPUB+DRM
  • Izdošanas datums: 12-Jun-2017
  • Izdevniecība: Morgan Kaufmann Publishers In
  • Valoda: eng
  • ISBN-13: 9780128093382
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Software Architecture for Big Data and the CloudPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 12-Jun-2017
  • Izdevniecība: Morgan Kaufmann Publishers In
  • Valoda: eng
  • ISBN-13: 9780128093382
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Software Architecture for Big Data and the Cloud is designed to be a single resource that brings together research on how software architectures can solve the challenges imposed by building big data software systems. The challenges of big data on the software architecture can relate to scale, security, integrity, performance, concurrency, parallelism, dependability among many others. Big data handling requires rethinking architectural solutions to meet functional and non-functional requirements related to volume, variety and velocity, their development and evolution trends in relation to the operating environment. With the wide adoption of the cloud as an enabler environment for exploiting data potentials as service under new economics models, little is known about the underlying architectures which can sustain this synergy. The editors have varied and complementary backgrounds in requirements and architecture, specifically in software architectures for cloud and big data, as well as expertise in software engineering for cloud and big data. This book collects together work across different disciplines in software engineering for cloud and big data including work expanded from conference tracks and workshops led by the editors.
  • Discusses systematic and disciplined approaches to building software architectures for cloud and big data with state-of-the-art methods and techniques
  • Presents case studies involving enterprise, business, and government service deployment of big data applications
  • Shares guidance on theory, frameworks, methodologies, and architecture for cloud and big data

Papildus informācija

Explores applied cloud computing for data intensive science and software architecture
Contributors xv
About the Editors xxi
Foreword xxv
Mandy Chessell
Foreword xxix
Ian Gorton
Preface xxxi
Chapter 1 Introduction. Software Architecture for Cloud and Big Data: An Open Quest for the Architecturally Significant Requirements
1(10)
Rami Bahsoon
Nour Ali
Maritta Heisel
Bruce Maxim
Ivan Mistrik
1.1 A Perspective into Software Architecture for Cloud and Big Data
1(1)
1.2 Cloud Architecturally Significant Requirements and Their Design Implications
2(5)
1.2.1 Dynamism and Elasticity as Cloud Architecturally Significant Requirements
3(1)
1.2.2 Multitenancy as Cloud Architecturally Significant Requirement
4(1)
1.2.3 Service Level Agreements (SLAs) Constraints as Cloud Architecturally Significant Requirement
4(1)
1.2.4 Cloud Marketplaces as Architecturally Significant Requirement
5(1)
1.2.5 Seeking Value as Cloud Architecturally Significant Requirement
6(1)
1.3 Big Data Management as Cloud Architecturally Significant Requirement
7(4)
1.3.1 Big Data Analytics Enabled by the Cloud and Its Architecturally Significant Requirements
8(1)
1.3.2 Architecturally Significant Requirements in Realm of Competing Big Data Technologies
8(1)
References
9(2)
PART 1 CONCEPTS AND MODELS
11(92)
Chapter 2 Hyperscalability -- The Changing Face of Software Architecture
13(20)
Ian Gorton
2.1 Introduction
13(1)
2.2 Hyperscalable Systems
14(6)
2.2.1 Scalability
14(1)
2.2.2 Scalability Limits
15(2)
2.2.3 Scalability Costs
17(2)
2.2.4 Hyperscalability
19(1)
2.3 Principles of Hyperscalable Systems
20(9)
2.3.1 Automate and Optimize to Control Costs
21(2)
2.3.2 Simple Solutions Promote Scalability
23(2)
2.3.3 Utilize Stateless Services
25(1)
2.3.4 Observability is Fundamental to Success at Hyperscale
26(3)
2.4 Related Work
29(1)
2.5 Conclusions
30(3)
References
30(3)
Chapter 3 Architecting to Deliver Value From a Big Data and Hybrid Cloud Architecture
33(16)
Mandy Chessell
Dan Wolfson
Tim Vincent
3.1 Introduction
33(1)
3.2 Supporting the Analytics Lifecycle
33(2)
3.3 The Role of Data Lakes
35(1)
3.4 Key Design Features That Make a Data Lake Successful
36(1)
3.5 Architecture Example -- Context Management in the IoT
37(1)
3.6 Big Data Origins and Characteristics
38(1)
3.7 The Systems That Capture and Process Big Data
39(2)
3.8 Operating Across Organizational Silos
41(1)
3.9 Architecture Example -- Local Processing of Big Data
42(1)
3.10 Architecture Example -- Creating a Multichannel View
43(2)
3.11 Application Independent Data
45(1)
3.12 Metadata and Governance
45(1)
3.13 Conclusions
46(1)
3.14 Outlook and Future Directions
47(2)
References
48(1)
Chapter 4 Domain-Driven Design of Big Data Systems Based on a Reference Architecture
49(20)
Cigdem Avci Salma
Bedir Tekinerdogan
Ioannis N. Athanasiadis
4.1 Introduction
49(1)
4.2 Domain-Driven Design Approach
50(3)
4.3 Related Work
53(1)
4.4 Feature Model of Big Data Systems
54(6)
4.4.1 Data
56(1)
4.4.2 Information Management
56(1)
4.4.3 Interface and Visualization
56(1)
4.4.4 Data Processing
56(1)
4.4.5 Data Storage
57(1)
4.4.6 Data Analysis
57(1)
4.4.7 Feature Constraints
57(3)
4.5 Deriving the Application Architectures and Example
60(6)
4.5.1 Feature Modeling
60(1)
4.5.2 Design Rule Modeling
60(1)
4.5.3 Associating Design Decisions With Features
61(1)
4.5.4 Generation of the Application Architecture and the Deployment Diagram
62(1)
4.5.5 Deriving Big Data Architectures of Existing Systems
63(3)
4.6 Conclusion
66(3)
References
67(2)
Chapter 5 An Architectural Model-Based Approach to Quality-Aware DevOps in Cloud Applications
69(22)
Robert Heinrich
Reiner Jung
Christian Zirkelbach
Wilhelm Hasselbring
Ralf Reussner
5.1 Introduction
69(1)
5.2 A Cloud-Based Software Application
70(1)
5.3 Differences in Architectural Models Among Development and Operations
71(1)
5.4 The iObserve Approach
72(3)
5.5 Addressing the Differences in Architectural Models
75(5)
5.5.1 The iObserve Megamodel
75(2)
5.5.2 Descriptive and Prescriptive Architectural Models in iObserve
77(1)
5.5.3 Static and Dynamic Content in Architectural Models
78(2)
5.6 Applying iObserve to CoCoME
80(4)
5.6.1 Applying the iObserve Megamodel
81(1)
5.6.2 Applying Descriptive and Prescriptive Architectural Models
82(1)
5.6.3 Applying Live Visualization
82(2)
5.7 Limitations
84(1)
5.8 Related Work
85(2)
5.9 Conclusion
87(4)
References
87(4)
Chapter 6 Bridging Ecology and Cloud: Transposing Ecological Perspective to Enable Better Cloud Autoscaling
91(12)
Tao Chen
Rami Bahsoon
6.1 Introduction
91(1)
6.2 Motivation
92(1)
6.3 Natural Ecosystem
93(1)
6.4 Transposing Ecological Principles, Theories and Models to Cloud Ecosystem
94(3)
6.5 Ecology-Inspired Self-Aware Pattern
97(2)
6.6 Opportunities and Challenges
99(1)
6.7 Related Work
100(1)
6.8 Conclusion
100(3)
References
101(1)
Acknowledgement
102(1)
PART 2 ANALYZING AND EVALUATING
103(78)
Chapter 7 Evaluating Web PKIs
105(22)
Jiangshan Yu
Mark Ryan
7.1 Introduction
105(1)
7.2 An Overview of PKI
106(2)
7.3 Desired Features and Security Concerns
108(2)
7.4 Existing Proposals
110(10)
7.4.1 Classic
110(1)
7.4.2 Difference Observation
110(4)
7.4.3 Scope Restriction
114(1)
7.4.4 Certificate Management Transparency
115(5)
7.5 Observations
120(3)
7.5.1 Property Perspective
120(2)
7.5.2 System Perspective
122(1)
7.6 Conclusion
123(4)
References
123(4)
Chapter 8 Performance Isolation in Cloud-Based Big Data Architectures
127(20)
Bedir Tekinerdogan
Alp Oral
8.1 Introduction
127(1)
8.2 Background
128(2)
8.2.1 Cloud Computing
128(1)
8.2.2 Big Data Architecture
129(1)
8.3 Case Study and Problem Statement
130(2)
8.3.1 Case Study
130(2)
8.3.2 Problem Statement
132(1)
8.4 Performance Monitoring in Cloud-Based Systems
132(2)
8.5 Application Framework for Performance Isolation
134(3)
8.6 Evaluation of the Framework
137(6)
8.6.1 Evaluation Results
140(3)
8.7 Discussion
143(1)
8.8 Related Work
143(1)
8.9 Conclusion
144(3)
References
145(2)
Chapter 9 From Legacy to Cloud: Risks and Benefits in Software Cloud Migration
147(20)
Anastasija Efremovska
Patricia Lago
9.1 Introduction
147(1)
9.2 Research Method
147(7)
9.2.1 Pilot Study
148(1)
9.2.2 Search Strategy
148(3)
9.2.3 Data Extraction
151(1)
9.2.4 Data Analysis Method
152(2)
9.3 Results
154(8)
9.3.1 Overview of Primary Studies and Quality Evaluation
154(2)
9.3.2 Benefits and Risks
156(3)
9.3.3 General Measures
159(1)
9.3.4 Models and Frameworks for Cloud Migration
160(2)
9.4 Discussion
162(2)
9.4.1 Findings and Lessons Learned
162(1)
9.4.2 Threats to Validity
163(1)
9.5 Conclusion
164(3)
References
164(3)
Chapter 10 Big Data: A Practitioners Perspective
167(14)
Darshan Lopes
Kevin Palmer
Fiona O'Sullivan
10.1 Big Data Is a New Paradigm -- Differences With Traditional Data Warehouse, Pitfalls and Consideration
167(2)
10.1.1 Differences With Traditional Data Warehouse
167(1)
10.1.2 Pitfalls
168(1)
10.1.3 Considerations
169(1)
10.2 Product Considerations for Big Data -- Use of Open Source Products for Big Data, Pitfalls and Considerations
169(2)
10.2.1 The Use of Open Source Product for Big Data
169(1)
10.2.2 Pitfalls
170(1)
10.2.3 Considerations
171(1)
10.3 Use of Cloud for hosting Big Data -- Why to Use Cloud, Pitfalls and Consideration
171(1)
10.3.1 Why to Use Cloud?
171(1)
10.3.2 Pitfalls
172(1)
10.3.3 Consideration
172(1)
10.4 Big Data Implementation -- Architecture Definition, Processing Framework and Migration Pattern From Data Warehouse to Big Data
172(6)
10.4.1 Patterns for Transitioning From Data Warehouse to Big Data
175(3)
10.5 Conclusion
178(3)
References
179(2)
PART 3 TECHNOLOGIES
181(140)
Chapter 11 A Taxonomy and Survey of Stream Processing Systems
183(24)
Xinwei Zhao
Saurabh Garg
Carlos Queiroz
Rajkumar Buyya
11.1 Introduction
183(2)
11.2 Stream Processing Platforms: A Brief Background
185(3)
11.2.1 Requirements of Stream Processing Platforms/Engines
186(1)
11.2.2 Generic Model of Modern Stream Processing Platforms/Engines
187(1)
11.3 Taxonomy
188(9)
11.3.1 Functional Aspects
189(6)
11.3.2 Nonfunctional Aspects
195(2)
11.4 A Survey of Stream Processing Platforms
197(3)
11.4.1 Data Stream Management Systems
198(1)
11.4.2 Complex Event Processing Systems
198(1)
11.4.3 Stream Processing Platforms/Engines
199(1)
11.5 Comparison Study of the Stream Processing Platforms
200(2)
11.5.1 Scalability
200(1)
11.5.2 Messaging & Distribution
201(1)
11.5.3 Data Processing/Stream Processors
201(1)
11.5.4 Fault Tolerance
202(1)
11.6 Conclusions and Future Directions
202(5)
References
204(3)
Chapter 12 Architecting Cloud Services for the Digital Me in a Privacy-Aware Environment
207(20)
Robert Eikermann
Markus Look
Alexander Roth
Berhard Rumpe
Andreas Wortmann
12.1 Introduction
207(1)
12.2 Example
208(2)
12.3 Challenges
210(2)
12.3.1 Service Composition
211(1)
12.3.2 Technology Abstraction
211(1)
12.3.3 Service and Data Integration
211(1)
12.3.4 Trusted Use of Personal Data
212(1)
12.4 Preliminaries
212(1)
12.5 System-of-Systems Approach
213(6)
12.5.1 Persistence Service
214(2)
12.5.2 DataConversion Service
216(1)
12.5.3 Privacy Service
217(1)
12.5.4 LookUp Service
217(1)
12.5.5 PersonalData Service
218(1)
12.6 Generative Approach
219(2)
12.7 Related Work
221(2)
12.7.1 Service Composition
222(1)
12.7.2 Technology Abstraction
222(1)
12.7.3 Service and Data Integration
222(1)
12.7.4 Trusted Use of Personal Data
222(1)
12.8 Discussion
223(1)
12.9 Conclusion
223(4)
References
224(3)
Chapter 13 Reengineering Data-Centric Information Systems for the Cloud -- A Method and Architectural Patterns Promoting Multitenancy
227(26)
Andrei Furda
Colin Fidge
Alistair Barros
Olaf Zimmermann
13.1 Introduction
227(1)
13.2 Context and Problem: Multitenancy in Cloud Computing
228(2)
13.3 Solution Overview: Reengineering Method and Process
230(2)
13.4 Solution Detail 1: Architectural Patterns in the Method
232(5)
13.4.1 Architectural Reengineering Steps for the Cloud (Architectural Refactoring)
233(1)
13.4.2 Multitenancy Requirements and Patterns for Cloud Environments
234(1)
13.4.3 The Multitenancy Capable Model
235(1)
13.4.4 The Multitenancy Capable Controller
235(1)
13.4.5 The Multitenancy Capable View
236(1)
13.5 Solution Detail 2: Testing and Code Reviews
237(3)
13.5.1 Testing for Multitenancy Defects
237(2)
13.5.2 Code Review for Multitenancy Defects
239(1)
13.5.3 Summary
240(1)
13.6 Case Study (Implementation)
240(6)
13.6.1 Multitenancy Transformation Without Patterns
241(2)
13.6.2 Multitenancy Transformation With Patterns
243(1)
13.6.3 Comparison
243(3)
13.7 Discussion
246(1)
13.8 Related Work
247(1)
13.9 Summary and Conclusions
247(6)
Appendix 13.A Architectural Refactoring (AR) Reference
248(1)
References
249(4)
Chapter 14 Exploring the Evolution of Big Data Technologies
253(32)
Stephen Bonner
Ibad Kureshi
John Brennan
Georgios Theodoropoulos
14.1 Introduction
253(1)
14.2 Big Data in Our Daily Lives
254(2)
14.3 Data Intensive Computing
256(7)
14.3.1 Big Compute Versus Big Data
256(2)
14.3.2 Data Intensive Applications
258(1)
14.3.3 Data Intensive Frameworks
258(1)
14.3.4 MapReduce and GFS
259(4)
14.4 Apache Hadoop
263(4)
14.4.1 Hadoop VI
263(3)
14.4.2 Hadoop 2.0
266(1)
14.5 Apache Spark
267(4)
14.5.1 Resilient Distributed Datasets
268(1)
14.5.2 Data Flow and Programming With Spark
269(1)
14.5.3 Spark Processing Engines
270(1)
14.5.4 Hadoop Ecosystem Taxonomy
271(1)
14.6 The Role of Cloud Computing
271(3)
14.7 The Future of Big Data Platforms
274(6)
14.7.1 Big Data Applications
274(1)
14.7.2 Big Data Frameworks and Hardware
275(4)
14.7.3 Big Data on the Road to Exascale
279(1)
14.8 Conclusion
280(5)
References
281(4)
Chapter 15 A Taxonomy and Survey of Fault-Tolerant Workflow Management Systems in Cloud and Distributed Computing Environments
285(36)
Deepak Poola
Mohsen Amini Salehi
Kotagiri Ramamohanarao
Rajkumar Buyya
15.1 Introduction
285(1)
15.2 Background
286(2)
15.2.1 Workflow Management Systems
286(1)
15.2.2 Workflow Scheduling
287(1)
15.3 Introduction to Fault-Tolerance
288(2)
15.3.1 Necessity for Fault-Tolerance in Distributed Systems
290(1)
15.4 Taxonomy of Faults
290(1)
15.5 Taxonomy of Fault-Tolerant Scheduling Algorithms
291(12)
15.5.1 Replication
292(3)
15.5.2 Resubmission
295(2)
15.5.3 Checkpointing
297(3)
15.5.4 Provenance
300(1)
15.5.5 Rescue Workflow
301(1)
15.5.6 User-Defined Exception Handling
301(1)
15.5.7 Alternate Task
301(1)
15.5.8 Failure Masking
301(1)
15.5.9 Slack Time
302(1)
15.5.10 Trust-Based Scheduling Algorithms
302(1)
15.6 Modeling of Failures in Workflow Management Systems
303(1)
15.7 Metrics Used to Quantify Fault-Tolerance
304(1)
15.8 Survey of Workflow Management Systems and Frameworks
305(8)
15.8.1 Askalon
305(3)
15.8.2 Pegasus
308(1)
15.8.3 Triana
309(1)
15.8.4 UNICORE 6
310(1)
15.8.5 Kepler
310(1)
15.8.6 Cloudbus Workflow Management System
310(1)
15.8.7 Traverna
311(1)
15.8.8 The e-Science Central (e-SC)
311(1)
15.8.9 SwinDeW-C
311(1)
15.8.10 Big Data Workflow Frameworks: MapReduce, Hadoop, and Spark
312(1)
15.8.11 Other Workflow Management Systems
313(1)
15.9 Tools and Support Systems
313(2)
15.9.1 Data Management Tools
313(1)
15.9.2 Security and Fault-Tolerance Management Tools
314(1)
15.9.3 Cloud Development Tools
314(1)
15.9.4 Support Systems
314(1)
15.10 Summary
315(6)
References
315(6)
PART 4 RESOURCE MANAGEMENT
321(68)
Chapter 16 The HARNESS Platform: A Hardware- and Network-Enhanced Software System for Cloud Computing
323(30)
Jose G.F. Coutinho
Mark Stillwell
Katerina Argyraki
George Ioannidis
Anca Iordache
Christoph Kleineweber
Alexandros Koliousis
John McGlone
Guillaume Pierre
Carmelo Ragusa
Peter Sanders
Thorsten Schutt
Teng Yu
Alexander Wolf
16.1 Introduction
323(1)
16.2 Related Work
324(1)
16.3 Overview
325(2)
16.4 Managing Heterogeneity
327(9)
16.4.1 Hierarchical Resource Management
328(1)
16.4.2 Agnostic Resource Management
329(2)
16.4.3 Ranking Allocation Requests
331(3)
16.4.4 HARNESS API
334(2)
16.5 Prototype Description
336(4)
16.5.1 The Platform Layer
336(2)
16.5.2 The Infrastructure Layer
338(1)
16.5.3 The Virtual Execution Layer
339(1)
16.6 Evaluation
340(9)
16.6.1 Executing HPC Applications on the Cloud
340(5)
16.6.2 Resource Scheduling with Network Constraints
345(4)
16.7 Conclusion
349(4)
Project Resources
350(1)
References
350(1)
Acknowledgements
351(2)
Chapter 17 Auditable Version Control Systems in Untrusted Public Clouds
353(14)
Bo Chen
Reza Curtmola
Jun Dai
17.1 Motivation and Contributions
353(2)
17.2 Background Knowledge
355(2)
17.2.1 Data Organization in Version Control Systems
355(2)
17.2.2 Remote Data Integrity Checking (RDIC)
357(1)
17.3 System and Adversarial Model
357(1)
17.4 Auditable Version Control Systems
358(4)
17.4.1 Definition of AVCS
358(1)
17.4.2 An AVCS Construction
359(3)
17.5 Discussion
362(1)
17.6 Other RDIC Approaches for Version Control Systems
363(1)
17.7 Evaluation
363(2)
17.7.1 Theoretical Evaluation
363(1)
17.7.2 Experimental Evaluation
364(1)
17.8 Conclusion
365(2)
References
365(2)
Chapter 18 Scientific Workflow Management System for Clouds
367(22)
Maria A. Rodriguez
Rajkumar Buyya
18.1 Introduction
367(1)
18.2 Background
368(2)
18.3 Workflow Management Systems for Clouds
370(2)
18.4 Cloudbus Workflow Management System
372(2)
18.5 Cloud-Based Extensions to the Workflow Engine
374(5)
18.6 Performance Evaluation
379(6)
18.6.1 WRPS
379(1)
18.6.2 Montage
379(1)
18.6.3 Setup of Experimental Infrastructure
380(1)
18.6.4 Montage Setup
381(1)
18.6.5 Results
382(3)
18.7 Summary and Conclusions
385(4)
References
386(3)
PART 5 LOOKING AHEAD
389(14)
Chapter 19 Outlook and Future Directions
391(12)
Maritta Heisel
Rami Bahsoon
Nour Ali
Bruce Maxim
Ivan Mistrik
19.1 New or Advanced Applications
391(3)
19.2 Advanced Supporting Technologies
394(1)
19.3 Architecturally Significant Requirements
395(2)
19.4 Challenges for the Architecting Process
397(4)
19.5 Further Reading
401(2)
References
402(1)
Glossary 403(4)
Author Index 407(18)
Subject Index 425
Ivan Mistrik is a computer scientist who is interested in system and software engineering (SE/SWE) and in system and software architecture (SA/SWA), in particular: life cycle system/software engineering, requirements engineering, relating software requirements and architectures, knowledge management in software development, rationale-based software development, aligning enterprise/system/software architectures, and collaborative system/software engineering. He has more than forty years experience in the field of computer systems engineering as an information systems developer, R&D leader, SE/SA research analyst, educator in computer sciences, and ICT management consultant.

In the past 40 years, he has been primarily working at various R&D institutions and has done consulting on a variety of large international projects sponsored by ESA, EU, NASA, NATO, and UN. He has also taught university-level computer sciences courses in software engineering, software architecture, distributed information systems, and human-computer interaction. He is the author or co-author of more than 80 articles and papers in international journals, conferences, books and workshops, most recently a chapter Capture of Software Requirements and Rationale through Collaborative Software Development, a paper Knowledge Management in the Global Software Engineering Environment, and a paper Architectural Knowledge Management in Global Software Development.

He has written a number of editorials and prefaces, most recently for the book on Aligning Enterprise, System, and Software Architecture and the book on Agile Software Architecture. He has also written over 120 technical reports and presented over 70 scientific/technical talks. He has served in many program committees and panels of reputable international conferences and organized a number of scientific workshops, most recently two workshops on Knowledge Engineering in Global Software and Development at International Conference on Global Software Engineering 2009 and 2010 and IEEE International Workshop on the Future of Software Engineering for/in the Cloud (FoSEC) held in conjunction with IEEE Cloud 2011.He has been the guest-editor of IEE Proceedings Software: A special Issue on Relating Software Requirements and Architectures published by IEE in 2005 and the lead-editor of the book Rationale Management in Software Engineering published by Springer in 2006. He has been the co-author of the book Rationale-Based Software Engineering published by Springer in May 2008. He has been the lead-editor of the book Collaborative Software Engineering published by Springer in 2010, the book on Relating Software Requirements and Architectures published by Springer in 2011 and the lead-editor of the book on Aligning Enterprise, System, and Software Architectures published by IGI Global in 2012. He was the lead-editor of the Expert Systems Special Issue on Knowledge Engineering in Global Software Development and the co-editor of the JSS Special Issue on the Future of Software Engineering for/in the Cloud, both published in 2013. He was the co-editor for the book on Agile Software Architecture published in 2013. Currently, he is the lead-editor for the book on Economics-driven Software Architecture to be published in 2014.

Rami Bahsoon is a Senior lecturer in Software Engineering and founder of the Software Engineering for/in the Cloud interest groups at the School of Computer Science, University of Birmingham, UK. His group currently comprises nine PhD students working in areas related to cloud software engineering and architectures. The groups research aims at developing architecture and frameworks to support and reason about the development and evolution of dependable ultra-large complex and data-intensive software systems, where the investigations span cloud computing architectures and their economics. Bahsoon had founded and co-organized the International Software Engineering Workshop series on Software Architectures and Mobility held in conjunction with ICSE and the IEEE International Software Engineering IN/FOR the Cloud workshop in conjunction with IEEE Services. He was the lead editor of two journal special issues with the Journal of Systems and Software Elsevier one on the Future of Software Engineering for/In the Cloud and another on Architecture and Mobility. Bahsoon has co-edited a book on Economics-driven Software Architecture, to be published by Elsevier in 2014 and co-edited another book on Aligning Enterprise, System, and Software Architectures, published by IGI Global in 2012. He is currently acting as the workshop chair for IEEE Services 2014, the Doctoral Symposium chair of IEEE/ACM Utility and Cloud Computing Conference (UCC 2014) and the track chair for Utility Computing of HPCC 2014. He holds a PhD in Software Engineering from University College London (UCL) for his research on evaluating software architecture stability using real options. He has also read for MBA-level certificates with London Business School. Nour Ali is a Senior Lecturer at the University of Brighton since December, 2012. She holds a PhD in Software Engineering from the Polytechnic University of Valencia-Spain for her work in Ambients in Aspect-Oriented Software Architecture. Her research area encompasses service oriented architecture, software architecture, model driven engineering and mobile systems. In 2014, the University of Brighton have awarded her a Rising Stars project in Service Oriented Architecture Recovery and Consistency. Maritta Heisel is a full professor for software engineering at the University Duisburg-Essen, Germany, since 2004. Her research interests include the development of dependable software, pattern- and component-based software development, requirements engineering (including quality requirements), software architecture, and software evolution. She is particularly interested in incorporating security and privacy considerations into software development processes and in integrating the development of safe and secure software. She has published over 100 scientific papers in various fields of software engineering. Bruce R. Maxim has worked as a software engineer, project manager, professor, author, and consultant for more than thirty years. His research interests include software engineering, human computer interaction, game design, social media, artificial intelligence, and computer science education. Bruce Maxim is associate professor of computer and information science at the University of MichiganDearborn. He established the GAME Lab in the College of Engineering and Computer Science. He has published more than fifty papers on computer algorithm animation, game development, and engineering education. He is co-author of Software Engineering: A Practitioner's Approach, a leading software engineering textbook.
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